Table of Contents

1. Audience

This document assumes basic knowledge of TRex, and assumes that TRex is installed and configured. For information, see the manual, especially the material up to the Basic Usage section.

2. Stateless support

2.1. High level functionality

  • Large scale - Supports about 10-22 million packets per second (mpps) per core, scalable with the number of cores

  • Support for 1, 10, 25, 40, and 100 Gb/sec interfaces

  • Support for multiple traffic profiles per interface

  • Profile can support multiple streams, scalable to 10K parallel streams

  • Supported for each stream:

    • Packet template - ability to build any packet (including malformed) using Scapy (example: MPLS/IPv4/Ipv6/GRE/VXLAN/NSH)

    • Field engine program

      • Ability to change any field inside the packet (example: src_ip = 10.0.0.1-10.0.0.255)

      • Ability to change the packet size (example: random packet size 64-9K)

    • Mode - Continuous/Burst/Multi-burst support

    • Rate can be specified as:

      • Packets per second (example: 14MPPS)

      • L1 bandwidth (example: 500Mb/sec)

      • L2 bandwidth (example: 500Mb/sec)

      • Interface link percentage (example: 10%)

    • Support for HLTAPI-like profile definition

    • Action - stream can trigger a stream

  • Interactive support - Fast Console, GUI

  • Statistics per interface

  • Statistics per stream done in hardware

  • Latency and Jitter per stream

  • Blazingly fast automation support

    • Python 2.7/3.0 Client API

    • Python HLTAPI Client API

  • Multi-user support - multiple users can interact with the same TRex instance simultaneously

2.1.1. Traffic profile example

The following example shows three streams configured for Continuous, Burst, and Multi-burst traffic.

Figure 1. Example of multiple streams

2.1.2. High level functionality - Roadmap for future development

  • Routing protocol support — RIP/BGP/ISIS/SPF, The current plan is not to open this support

2.2. IXIA IXExplorer vs TRex

TRex has limited functionality compared to IXIA, but has some advantages. The following table summarizes the differences:

Table 1. TRex vs IXExplorer
Feature IXExplorer TRex Description

Line rate

Yes

10-24MPPS/core, depends on the use case

Multi stream

255

Software limited to ~20K

Packet build flexibility

Limited

Scapy - Unlimited

Example: GRE/VXLAN/NSH is supported. Can be extended to future protocols

Packet Field engine

limited

Unlimited

Tx Mode

Continuous/Burst/Multi-burst

Continuous/Burst/Multi-burst

ARP/IPv6 ND Emulation

Yes

Yes

DHCP Client Emulation

Yes

Yes

Extendable Emulation framework

No

Yes

Automation

TCL/Python wrapper to TCL

native Python/Scapy

Automation speed sec

30 sec

1 msec

Test of load/start/stop/get counters

HLTAPI

Full support. 2000 pages of documentation

Limited. 20 pages of documentation

Per Stream statistics

255 streams with 4 global masks

128 rules for XL710/X710 hardware and software impl for 82599/I350/X550

Some packet type restrictions apply to XL710/X710. Software mode can be extended to 32K rules

Latency Jitter

Yes,Resolution of nsec (hardware)

Yes,Resolution of usec (software)

Multi-user support

Yes

Yes

GUI

very good

WIP, packet builder, Field Engine, Global port statistic, Latency, per stream statistic . Not the same as IXIA. trex-stateless-gui

Cisco pyATS support

Yes

Yes - Python 2.7/Python 3.4

Routing Emulation

Yes

WIP. Will integrate Cisco Proprietary tool on top of TRex

2.3. RPC Architecture

A JSON-RPC2 thread in the TRex control plane core provides support for interactive mode.

Figure 2. RPC Server Components
Layers
  • Control transport protocol: ZMQ working in REQ/RES mode.

  • RPC protocol on top of the control transport protocol: JSON-RPC2.

  • Asynchronous transport: ZMQ working in SUB/PUB mode (used for asynchronous events such as interface change mode, counters, and so on).

Interfaces
  • Automation API: Python is the first client to implement the Python automation API.

  • User interface: The console uses the Python API to implement a user interface for TRex.

  • GUI : The GUI works on top of the JSON-RPC2 layer.

Control of TRex interfaces
  • Numerous users can control a single TRex server together, from different interfaces.

  • Users acquire individual TRex interfaces exclusively. Example: Two users control a 4-port TRex server. User A acquires interfaces 0 and 1; User B acquires interfaces 3 and 4.

  • Only one user interface (console or GUI) can have read/write control of a specific interface. This enables caching the TRex server interface information in the client core. Example: User A, with two acquired interfaces, can have only one read/write control session at a time.

  • A user can set up numerous read-only clients on a single interface - for example, for monitoring traffic statistics on the interface.

  • A client in read-write mode can acquire a statistic in real time (with ASYNC ZMQ). This enables viewing statistics through numerous user interfaces (console and GUI) simultaneously.

Synchronization
  • A client syncs with the TRex server to get the state in connection time, and caches the server information locally after the state has changed.

  • If a client crashes or exits, it syncs again after reconnecting.

Figure 3. Multiple users, per interface

For details about the TRex RPC server, see the RPC specification.

2.3.1. RPC architecture highlights

This Architecture provides the following advantages:

  • Fast interaction with TRex server. Loading, starting, and stopping a profile for an interface is very fast - about 2000 cycles/sec.

  • Leverages Python/Scapy for building a packet/field engine.

  • HLTAPI compiler complexity is handled in Python.

2.4. TRex Objects

Figure 4. TRex Objects
  • TRex: Each TRex instance supports numerous interfaces.

  • Interface: Each interface supports one or more traffic profiles.

  • Traffic profile: Each traffic profile supports one or more streams.

  • Stream: Each stream includes:

    • Packet: Packet template up to 9 KB

    • Field Engine: Which field to change, do we want to change packet size

    • Mode: Specifies how to send packets: Continuous/Burst/Multi-burst

    • Rx Stats: Statistics to collect for each stream

    • Rate: Rate (packets per second or bandwidth)

    • Action: Specifies stream to follow when the current stream is complete (valid for Continuous or Burst modes).

2.5. Stateful vs Stateless

TRex Stateless support enables basic L2/L3 testing, relevant mostly for a switch or router. Using Statelss mode, it is possible to define a stream with a one packet template, define a program to change any fields in the packet, and run the stream in continuous, burst, or multi-burst mode. With Stateless, you cannot learn NAT translation; there is no context of flow/client/server.

  • In Stateful mode, the basic building block is a flow/application (composed of many packets).

  • Stateless mode is much more flexible, enabling you to define any type of packet, and build a simple program.

Table 2. Stateful vs Stateless features
Feature Stateless Stateful

Per flow state

No

Yes

NAT

No

Yes

Tunnel

Yes

Some are supported

L7 App emulation

No

Yes

Any type of packet

Yes

No

Latency Jitter

Per Stream

Global/Per flow

2.5.1. Using Stateless mode to mimic Stateful mode

Stateless mode can mimic some, but not all functionality of Stateful mode. For example, you can load a pcap with the number of packets as a link of streams: a→b→c→d→ back to a You can then create a program for each stream to change src_ip=10. 0.0.1-10.0.0.254. This creates traffic similar to that of Stateful mode, but with a completely different basis.

If you are confused you probably need Stateless. :-)

2.6. TRex package folders

Location Description

/

t-rex-64/dpdk_set_ports/stl-sim

/stl

Stateless native (py) profiles

/stl/yaml

Stateless YAML profiles

/stl/hlt

Stateless HLT profiles

/ko

Kernel modules for DPDK

/external_libs

Python external libs used by server/clients

/exp

Golden pcap file for unit-tests

/cfg

Examples of config files

/cap2

Stateful profiles

/avl

Stateful profiles - SFR profile

/automation

Python client/server code for both Stateful and Stateless

/automation/regression

Regression for Stateless and Stateful

/automation/config

Regression setups config files

/automation/trex_control_plane/stl

Stateless lib and Console

/automation/trex_control_plane/stl/trex_stl_lib

Stateless lib

/automation/trex_control_plane/stl/examples

Stateless Examples

2.7. Getting started Tutorials

The tutorials in this section demonstrate basic TRex stateless use cases. Examples include common and moderately advanced TRex concepts.

2.7.1. Tutorial: Prepare TRex configuration file

Goal

Define the TRex physical or virtual ports and create configuration file.

Follow this chapter first time configuration

2.7.2. Tutorial: Load TRex server, Simple IPv4 UDP

Goal

Send a simple UDP packets from all ports of a TRex server.

Traffic profile

The following profile defines one stream, with an IP/UDP packet template with 10 bytes of x(0x78) of payload. For more examples of defining packets using Scapy see the Scapy documentation.

File

stl/udp_1pkt_simple.py

from trex_stl_lib.api import *

class STLS1(object):

    def create_stream (self):

        return STLStream(
            packet =
                    STLPktBuilder(
                        pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/
                                UDP(dport=12,sport=1025)/(10*'x')                       1
                    ),
             mode = STLTXCont())                                                        2


    def get_streams (self, direction = 0, **kwargs):                                              3
        # create 1 stream
        return [ self.create_stream() ]


# dynamic load - used for TRex console or simulator
def register():                                                                         4
    return STLS1()
1 Defines the packet. In this case, the packet is IP/UDP with 10 bytes of x. For more information, see the Scapy documentation.
2 Mode: Continuous. Rate: 1 PPS (default rate is 1 PPS)
3 The get_streams function is mandatory
4 Each traffic profile module requires a register function.
Note

The SRC/DST MAC addresses are taken from /etc/trex_cfg.yaml. To change them, add Ether(dst="00:00:dd:dd:00:01") with the desired destination.

Start TRex as a server
Note

The TRex package includes all required packages. It is unnecessary to install any python packages (including Scapy).

[bash]>sudo ./t-rex-64 -i
  • Wait until the server is up and running.

  • (Optional) Use -c to add more cores.

  • (Optional) Use --cfg to specify a different configuration file. The default is /etc/trex_cfg.yaml.

Connect with console

On the same machine, in a new terminal window (open a new window using xterm, or ssh again), connect to TRex using trex-console.

[bash]>trex-console                                                           #1

Connecting to RPC server on localhost:4501                   [SUCCESS]
connecting to publisher server on localhost:4500             [SUCCESS]
Acquiring ports [0, 1, 2, 3]:                                [SUCCESS]

125.69 [ms]

trex>start -f stl/udp_1pkt_simple.py -m 10mbps -a                      #2

Removing all streams from port(s) [0, 1, 2, 3]:              [SUCCESS]
Attaching 1 streams to port(s) [0, 1, 2, 3]:                 [SUCCESS]
Starting traffic on port(s) [0, 1, 2, 3]:                    [SUCCESS]

# pause  the traffic on all port
>pause -a                                                               #3

# resume  the traffic on all port
>resume -a                                                              #4

# stop traffic on all port
>stop -a                                                                #5

# show dynamic statistic
>tui
1 Connects to the TRex server from the local machine.
2 Start the traffic on all ports at 10 mbps. Can also specify as MPPS. Example: 14 MPPS (-m 14mpps).
3 Pauses the traffic.
4 Resumes.
5 Stops traffic on all the ports.
Note

If you have a connection error, open the /etc/trex_cfg.yaml file and remove keywords such as enable_zmq_pub : true and zmq_pub_port : 4501 from the file.

Viewing streams

To display stream data for all ports, use streams -a.

Streams
trex>streams -a
Port 0:

    ID |     packet type     |  length  |       mode       |  rate     | next stream
  -----------------------------------------------------------------------------------
    1  | Ethernet:IP:UDP:Raw |       56 |    Continuous    |  1.00 pps |      -1

Port 1:

    ID |     packet type     |  length  |       mode       |  rate     | next stream
  -----------------------------------------------------------------------------------
    1  | Ethernet:IP:UDP:Raw |       56 |    Continuous    |  1.00 pps |      -1

Port 2:

    ID |     packet type     |  length  |       mode       |  rate     | next stream
  -----------------------------------------------------------------------------------
    1  | Ethernet:IP:UDP:Raw |       56 |    Continuous    |  1.00 pps |      -1

Port 3:

    ID |     packet type     |  length  |       mode       |  rate     | next stream
  -----------------------------------------------------------------------------------
    1  | Ethernet:IP:UDP:Raw |       56 |    Continuous    |  1.00 pps |      -1
Viewing command help

To view help for a command, use <command> --help.

Viewing general statistics

To view general statistics, open a "textual user interface" with tui.

TRex >tui
Global Statistics

Connection  : localhost, Port 4501
Version     : v1.93, UUID: N/A
Cpu Util    : 0.2%
            :
Total Tx L2 : 40.01 Mb/sec
Total Tx L1 : 52.51 Mb/sec
Total Rx    : 40.01 Mb/sec
Total Pps   : 78.14 Kpkt/sec
            :
Drop Rate   : 0.00 b/sec
Queue Full  : 0 pkts

Port Statistics

   port    |         0          |         1          |
 --------------------------------------------------------
 owner      |             hhaim |             hhaim |
 state      |            ACTIVE |            ACTIVE |
 --         |                   |                   |
 Tx bps L2  |        10.00 Mbps |        10.00 Mbps |
 Tx bps L1  |        13.13 Mbps |        13.13 Mbps |
 Tx pps     |        19.54 Kpps |        19.54 Kpps |
 Line Util. |            0.13 % |            0.13 % |
 ---        |                   |                   |
 Rx bps     |        10.00 Mbps |        10.00 Mbps |
 Rx pps     |        19.54 Kpps |        19.54 Kpps |
 ----       |                   |                   |
 opackets   |           1725794 |           1725794 |
 ipackets   |           1725794 |           1725794 |
 obytes     |         110450816 |         110450816 |
 ibytes     |         110450816 |         110450816 |
 tx-bytes   |         110.45 MB |         110.45 MB |
 rx-bytes   |         110.45 MB |         110.45 MB |
 tx-pkts    |        1.73 Mpkts |        1.73 Mpkts |
 rx-pkts    |        1.73 Mpkts |        1.73 Mpkts |
 -----      |                   |                   |
 oerrors    |                 0 |                 0 |
 ierrors    |                 0 |                 0 |

 status:  /

 browse:     'q' - quit, 'g' - dashboard, '0-3' - port display
 dashboard:  'p' - pause, 'c' - clear, '-' - low 5%, '+' - up 5%,
Discussion

In this example TRex sends the same packet from all ports. If your setup is connected with loopback, you will see Tx packets from port 0 in Rx port 1 and vice versa. If you have DUT with static route, you might see all the packets going to specific port.

Static route
interface TenGigabitEthernet0/0/0
 mtu 9000
 ip address 1.1.9.1 255.255.255.0
!
interface TenGigabitEthernet0/1/0
 mtu 9000
 ip address 1.1.10.1 255.255.255.0
!

ip route 16.0.0.0 255.0.0.0 1.1.9.2
ip route 48.0.0.0 255.0.0.0 1.1.10.2

In this example all the packets will be routed to TenGigabitEthernet0/1/0 port. The following example uses the direction flag to change this.


 class STLS1(object):

    def create_stream (self):
        return STLStream(
            packet =
                    STLPktBuilder(
                        pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/
                                UDP(dport=12,sport=1025)/(10*'x')
                    ),
             mode = STLTXCont())

    def get_streams (self, direction = 0, **kwargs):
        # create 1 stream
        if direction==0:                                                        1
            src_ip="16.0.0.1"
            dst_ip="48.0.0.1"
        else:
            src_ip="48.0.0.1"
            dst_ip="16.0.0.1"

        pkt   = STLPktBuilder(
                              pkt = Ether()/IP(src=src_ip,dst=dst_ip)/
                              UDP(dport=12,sport=1025)/(10*'x') )

        return [ STLStream( packet = pkt,mode = STLTXCont()) ]
1 This use of the direction flag causes a different packet to be sent for each direction.

2.7.3. Tutorial: Connect from a remote server

Goal

Connect by console from remote machine to a TRex server

Check that TRex server is operational

Ensure that the TRex server is running. If not, run TRex in interactive mode.

[bash]>sudo ./t-rex-64 -i
Connect with Console

From a remote machine, use trex-console to connect. Include the -s flag, as shown below, to specify the server.

[bash]>trex-console -s csi-kiwi-02  #1
1 TRex server is csi-kiwi-02.

The TRex client requires Python versions 2.7.x or 3.4.x. To change the Python version, set the PYTHON environment variable as follows:

tcsh shell
[tcsh]>setenv PYTHON /bin/python     #tcsh
bash shell
[bash]>extern PYTHON=/bin/mypython    #bash
Note

The client machine should run Python 2.7.x or 3.4.x. Cisco CEL/ADS is supported. The TRex package includes the required client archive.

2.7.4. Tutorial: Source and Destination MAC addresses

Goal

Change the source/destination MAC address

Each TRex port has a source and destination MAC (DUT) configured in the /etc/trex_cfg.yaml configuration file. The source MAC is not necessarily the hardware MAC address configured in EEPROM. By default, the hardware-specified MAC addresses (source and destination) are used. If a source or destination MAC address is configured explicitly, that address takes precedence over the hardware-specified default.

Table 3. MAC address
Scapy Source MAC Destination MAC

Ether()

trex_cfg (src)

trex_cfg(dst)

Ether(src="00:bb:12:34:56:01")

00:bb:12:34:56:01

trex_cfg(dst)

Ether(dst="00:bb:12:34:56:01")

trex_cfg(src)

00:bb:12:34:56:01

    def create_stream (self):

        base_pkt =  Ether(src="00:bb:12:34:56:01")/      1
                    IP(src="16.0.0.1",dst="48.0.0.1")/
                    UDP(dport=12,sport=1025)
1 Specifying the source interface MAC replaces the default specified in the configuration YAML file.
Important

TRex port will receive a packet only if the packet’s destination MAC matches the HW Src MAC defined for that port in the /etc/trex_cfg.yaml configuration file. Alternatively, a port can be put into promiscuous mode, allowing the port to receive all packets on the line. The port can be configured to promiscuous mode by API or by the following command at the console: portattr -a --prom.

To set ports to promiscuous mode and show the port status:

trex>portattr -a --prom on                                          #1
trex>stats --ps
Port Status

     port       |          0           |          1           |
  ---------------------------------------------------------------
driver          |    rte_ixgbe_pmd     |    rte_ixgbe_pmd     |
maximum         |       10 Gb/s        |       10 Gb/s        |
status          |         IDLE         |         IDLE         |
promiscuous     |         on           |         on           |     #2
  --            |                      |                      |
HW src mac      |  90:e2:ba:36:33:c0   |  90:e2:ba:36:33:c1   |
SW src mac      |  00:00:00:01:00:00   |  00:00:00:01:00:00   |
SW dst mac      |  00:00:00:01:00:00   |  00:00:00:01:00:00   |
  ---           |                      |                      |
PCI Address     |     0000:03:00.0     |     0000:03:00.1     |
NUMA Node       |          0           |          0           |
1 Configures all ports to promiscuous mode.
2 Indicates port promiscuous mode status.

To change ports to promiscuous mode by Python API:

Python API to change ports to promiscuous mode
        c = STLClient(verbose_level = LoggerApi.VERBOSE_REGULAR)

        c.connect()

        my_ports=[0,1]

        # prepare our ports
        c.reset(ports = my_ports)

        # port info, mac-addr info, speed
        print c.get_port_info(my_ports)                         1

        c.set_port_attr(my_ports, promiscuous = True)           2
1 Get port info for all ports.
2 Change the port attribute to promiscuous = True.

For more information see the Python Client API.

Note

An interface is not set to promiscuous mode by default. Typically, after changing the port to promiscuous mode for a specific test, it is advisable to change it back to non-promiscuous mode.

2.7.5. Tutorial: Python automation

Goal

Simple automation test using Python from a local or remote machine

Directories

Python API examples: automation/trex_control_plane/stl/examples.

Python API library: automation/trex_control_plane/stl/trex_stl_lib.

The TRex console uses the Python API library to interact with the TRex server using the JSON-RPC2 protocol over ZMQ.

Figure 5. RPC Server Components
import stl_path                                                            1
from trex_stl_lib.api import *                                             2

import time
import json

# simple packet creation                                                   3
def create_pkt (size, direction):

    ip_range = {'src': {'start': "10.0.0.1", 'end': "10.0.0.254"},
                'dst': {'start': "8.0.0.1",  'end': "8.0.0.254"}}

    if (direction == 0):
        src = ip_range['src']
        dst = ip_range['dst']
    else:
        src = ip_range['dst']
        dst = ip_range['src']

    vm = [
        # src                                                               4
        STLVmFlowVar(name="src",
                     min_value=src['start'],
                     max_value=src['end'],
                     size=4,op="inc"),
        STLVmWrFlowVar(fv_name="src",pkt_offset= "IP.src"),

        # dst
        STLVmFlowVar(name="dst",
                     min_value=dst['start'],
                     max_value=dst['end'],
                     size=4,op="inc"),
        STLVmWrFlowVar(fv_name="dst",pkt_offset= "IP.dst"),

        # checksum
        STLVmFixIpv4(offset = "IP")
        ]


    base = Ether()/IP()/UDP()
    pad = max(0, len(base)) * 'x'

    return STLPktBuilder(pkt = base/pad,
                         vm  = vm)


def simple_burst ():

    # create client
    c = STLClient()
                    # username/server can be changed those are the default
                    # username = common.get_current_user(),
                    # server = "localhost"
                    # STLClient(server = "my_server",username ="trex_client") for example
    passed = True

    try:
        # turn this on for some information
        #c.set_verbose("high")

        # create two streams
        s1 = STLStream(packet = create_pkt(200, 0),
                       mode = STLTXCont(pps = 100))

        # second stream with a phase of 1ms (inter stream gap)
        s2 = STLStream(packet = create_pkt(200, 1),
                       isg = 1000,
                       mode = STLTXCont(pps = 100))


        # connect to server
        c.connect()                                                                5

        # prepare our ports (my machine has 0 <--> 1 with static route)
        c.reset(ports = [0, 1]) #  Acquire port 0,1 for $USER                      6

        # add both streams to ports
        c.add_streams(s1, ports = [0])
        c.add_streams(s2, ports = [1])

        # clear the stats before injecting
        c.clear_stats()

        # choose rate and start traffic for 10 seconds on 5 mpps
        print "Running 5 Mpps on ports 0, 1 for 10 seconds..."
        c.start(ports = [0, 1], mult = "5mpps", duration = 10)                     7

        # block until done
        c.wait_on_traffic(ports = [0, 1])                                          8

        # read the stats after the test
        stats = c.get_stats()                                                      9

        print json.dumps(stats[0], indent = 4, separators=(',', ': '), sort_keys = True)
        print json.dumps(stats[1], indent = 4, separators=(',', ': '), sort_keys = True)

        lost_a = stats[0]["opackets"] - stats[1]["ipackets"]
        lost_b = stats[1]["opackets"] - stats[0]["ipackets"]

        print "\npackets lost from 0 --> 1:   {0} pkts".format(lost_a)
        print "packets lost from 1 --> 0:   {0} pkts".format(lost_b)

        if (lost_a == 0) and (lost_b == 0):
            passed = True
        else:
            passed = False

    except STLError as e:
        passed = False
        print e

    finally:
        c.disconnect()                                                             10

    if passed:
        print "\nTest has passed :-)\n"
    else:
        print "\nTest has failed :-(\n"


# run the tests
simple_burst()
1 Imports the stl_path. The path here is specific to this example. When configuring, provide the path to your stl_trex library.
2 Imports TRex Stateless library. When configuring, provide the path to your TRex Stateless library.
3 Creates packet per direction using Scapy.
4 See the Field Engine section for information.
5 Connects to the local TRex. Username and server can be added.
6 Acquires the ports.
7 Loads the traffic profile and start generating traffic.
8 Waits for the traffic to be finished. There is a polling function so you can test do something while waiting.
9 Get port statistics.
10 Disconnects.

See TRex Stateless Python API for details about using the Python APIs.

2.7.6. Tutorial: HLT Python API

HLT Python API is a layer on top of the native layer. It supports the standard Cisco traffic generator API. For more information, see Cisco/IXIA/Spirent documentation. TRex supports limited number of HLTAPI arguments and the recommendation is to use the native API due to the flexibility and simplicity.

Supported HLT Python API classes:

  • Device Control

    • connect

    • cleanup_session

    • device_info

    • info

  • Interface

    • interface_config

    • interface_stats

  • Traffic

    • traffic_config - not all arguments are supported

    • traffic_control

    • traffic_stats

For details, see Appendix


import sys
import argparse
import stl_path
from trex_stl_lib.api import *                                          1
from trex_stl_lib.trex_stl_hltapi import *                              2


if __name__ == "__main__":
    parser = argparse.ArgumentParser(usage="""
    Connect to TRex and send burst of packets

    examples

     hlt_udp_simple.py -s 9000 -d 30

     hlt_udp_simple.py -s 9000 -d 30 -rate_percent 10

     hlt_udp_simple.py -s 300 -d 30 -rate_pps 5000000

     hlt_udp_simple.py -s 800 -d 30 -rate_bps 500000000 --debug

     then run the simulator on the output
       ./stl-sim -f example.yaml -o a.pcap  ==> a.pcap include the packet

    """,
    description="Example for TRex HLTAPI",
    epilog=" based on hhaim's stl_run_udp_simple example")

    parser.add_argument("--ip",
                        dest="ip",
                        help='Remote trex ip',
                        default="127.0.0.1",
                        type = str)

    parser.add_argument("-s", "--frame-size",
                        dest="frame_size",
                        help='L2 frame size in bytes without FCS',
                        default=60,
                        type = int,)

    parser.add_argument('-d','--duration',
                        dest='duration',
                        help='duration in second ',
                        default=10,
                        type = int,)

    parser.add_argument('--rate-pps',
                        dest='rate_pps',
                        help='speed in pps',
                        default="100")

    parser.add_argument('--src',
                        dest='src_mac',
                        help='src MAC',
                        default='00:50:56:b9:de:75')

    parser.add_argument('--dst',
                        dest='dst_mac',
                        help='dst MAC',
                        default='00:50:56:b9:34:f3')

    args = parser.parse_args()

    hltapi = CTRexHltApi()
    print 'Connecting to TRex'
    res = hltapi.connect(device = args.ip, port_list = [0, 1], reset = True, break_locks = True)
    check_res(res)
    ports = res['port_handle']
    if len(ports) < 2:
        error('Should have at least 2 ports for this test')
    print 'Connected, acquired ports: %s' % ports

    print 'Creating traffic'

    res = hltapi.traffic_config(mode = 'create', bidirectional = True,
                                port_handle = ports[0], port_handle2 = ports[1],
                                frame_size = args.frame_size,
                                mac_src = args.src_mac, mac_dst = args.dst_mac,
                                mac_src2 = args.dst_mac, mac_dst2 = args.src_mac,
                                l3_protocol = 'ipv4',
                                ip_src_addr = '10.0.0.1', ip_src_mode = 'increment', ip_src_count = 254,
                                ip_dst_addr = '8.0.0.1', ip_dst_mode = 'increment', ip_dst_count = 254,
                                l4_protocol = 'udp',
                                udp_dst_port = 12, udp_src_port = 1025,
                                stream_id = 1, # temporary workaround, add_stream does not return stream_id
                                rate_pps = args.rate_pps,
                                )
    check_res(res)

    print 'Starting traffic'
    res = hltapi.traffic_control(action = 'run', port_handle = ports[:2])
    check_res(res)
    wait_with_progress(args.duration)

    print 'Stopping traffic'
    res = hltapi.traffic_control(action = 'stop', port_handle = ports[:2])
    check_res(res)

    res = hltapi.traffic_stats(mode = 'aggregate', port_handle = ports[:2])
    check_res(res)
    print_brief_stats(res)

    res = hltapi.cleanup_session(port_handle = 'all')
    check_res(res)

    print 'Done'
1 Imports the native TRex API.
2 Imports the HLT API.

2.7.7. Tutorial: Simple IPv4/UDP packet - Simulator

Goal

Use the TRex Stateless simulator.

Demonstrates the most basic use case using TRex simulator.

The TRex package includes a simulator tool, stl-sim. The simulator operates as a Python script that calls an executable. The platform requirements for the simulator tool are the same as for TRex.

The TRex simulator can:

  • Test your traffic profiles before running them on TRex.

  • Generate an output pcap file.

  • Simulate a number of threads.

  • Convert from one type of profile to another.

  • Convert any profile to JSON (API). For information, see: TRex stream specification

Example traffic profile:

from trex_stl_lib.api import *

class STLS1(object):

    def create_stream (self):

        return STLStream(
            packet =
                    STLPktBuilder(
                        pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/
                                UDP(dport=12,sport=1025)/(10*'x')                       1
                    ),
             mode = STLTXCont())                                                        2


    def get_streams (self, direction = 0, **kwargs):
        # create 1 stream
        return [ self.create_stream() ]


# dynamic load - used for TRex console or simulator
def register():                                                                         3
    return STLS1()
1 Defines the packet - in this case, IP/UDP with 10 bytes of x.
2 Mode is Continuous, with a rate of 1 PPS. (Default rate: 1 PPS)
3 Each traffic profile module requires a register function.

The following runs the traffic profile through the TRex simulator, limiting the number of packets to 10, and storing the output in a pcap file.

[bash]>./stl-sim -f stl/udp_1pkt_simple.py -o b.pcap -l 10
  executing command: 'bp-sim-64-debug --pcap --sl --cores 1 --limit 5000 -f /tmp/tmpq94Tfx -o b.pcap'

  General info:
  ------------

  image type:               debug
  I/O output:               b.pcap
  packet limit:             10
  core recording:           merge all

  Configuration info:
  -------------------

  ports:                    2
  cores:                    1

  Port Config:
  ------------

  stream count:             1
  max PPS    :              1.00  pps
  max BPS L1 :              672.00  bps
  max BPS L2 :              512.00  bps
  line util. :              0.00  %


  Starting simulation...


  Simulation summary:
  -------------------

  simulated 10 packets
  written 10 packets to 'b.pcap'

Contents of the output pcap file produced by the simulator in the previous step:

Figure 6. TRex simulator output stored in pcap file

Adding --json displays the details of the JSON command for adding a stream:

[bash]>./stl-sim -f stl/udp_1pkt_simple.py --json
[
    {
        "id": 1,
        "jsonrpc": "2.0",
        "method": "add_stream",
        "params": {
            "handler": 0,
            "port_id": 0,
            "stream": {
                "action_count": 0,
                "enabled": true,
                "flags": 0,
                "isg": 0.0,
                "mode": {
                    "rate": {
                        "type": "pps",
                        "value": 1.0
                    },
                    "type": "continuous"
                },
                "next_stream_id": -1,
                "packet": {
                    "binary": "AAAAAQAAAAAAAgAACABFAAAmAA",
                    "meta": ""
                },
                "rx_stats": {
                    "enabled": false
                },
                "self_start": true,
                "vm": {
                    "instructions": [],
                    "split_by_var": ""
                }
            },
            "stream_id": 1
        }
    },
    {
        "id": 1,
        "jsonrpc": "2.0",
        "method": "start_traffic",
        "params": {
            "duration": -1,
            "force": true,
            "handler": 0,
            "mul": {
                "op": "abs",
                "type": "raw",
                "value": 1.0
            },
            "port_id": 0
        }
    }
]

For more information about stream definition, see the RPC specification.

To convert the profile to YAML format:

$./stl-sim -f stl/udp_1pkt_simple.py --yaml
- stream:
    action_count: 0
    enabled: true
    flags: 0
    isg: 0.0
    mode:
      pps: 1.0
      type: continuous
    packet:
      binary: AAAAAQAAAAAAAgAACABFAAAmAAEAAEARO
      meta: ''
    rx_stats:
      enabled: false
    self_start: true
    vm:
      instructions: []
      split_by_var: ''

To display packet details, use the --pkt option (using Scapy).

[bash]>./stl-sim -f stl/udp_1pkt_simple.py --pkt
 =======================
 Stream 0
 =======================
###[ Ethernet ]###
  dst       = 00:00:00:01:00:00
  src       = 00:00:00:02:00:00
  type      = IPv4
###[ IP ]###
     version   = 4L
     ihl       = 5L
     tos       = 0x0
     len       = 38
     id        = 1
     flags     =
     frag      = 0L
     ttl       = 64
     proto     = udp
     chksum    = 0x3ac5
     src       = 16.0.0.1
     dst       = 48.0.0.1
     \options   \
###[ UDP ]###
        sport     = blackjack
        dport     = 12
        len       = 18
        chksum    = 0x6161
###[ Raw ]###
           load      = 'xxxxxxxxxx'
0000   00 00 00 01 00 00 00 00  00 02 00 00 08 00 45 00   ..............E.
0010   00 26 00 01 00 00 40 11  3A C5 10 00 00 01 30 00   .&....@.:.....0.
0020   00 01 04 01 00 0C 00 12  61 61 78 78 78 78 78 78   ........aaxxxxxx
0030   78 78 78 78                                        xxxx

To convert any profile type to native again, use the --native option:

Input YAML format
$more stl/yaml/imix_1pkt.yaml
- name: udp_64B
  stream:
    self_start: True
    packet:
      pcap: udp_64B_no_crc.pcap  # pcap should not include CRC
    mode:
      type: continuous
      pps: 100

To convert to native:

[bash]>./stl-sim -f stl/yaml/imix_1pkt.yaml --native
Output Native
# !!! Auto-generated code !!!
from trex_stl_lib.api import *

class STLS1(object):
    def get_streams(self):
        streams = []

        packet = (Ether(src='00:de:01:0a:01:00', dst='00:50:56:80:0d:28', type=2048) /
                  IP(src='101.0.0.1', proto=17, dst='102.0.0.1', chksum=28605, len=46, flags=2L, ihl=5L, id=0) /
                  UDP(dport=2001, sport=2001, len=26, chksum=1176) /
                  Raw(load='\xde\xad\xbe\xef\x00\x01\x06\x07\x08\x09\x0a\x0b\x00\x9b\xe7\xdb\x82M'))
        vm = STLScVmRaw([], split_by_field = '')
        stream = STLStream(packet = CScapyTRexPktBuilder(pkt = packet, vm = vm),
                           name = 'udp_64B',
                           mac_src_override_by_pkt = 0,
                           mac_dst_override_mode = 0,
                           mode = STLTXCont(pps = 100))
        streams.append(stream)

        return streams

def register():
    return STLS1()
Discussion

The following are the main traffic profile formats. Native is the preferred format. There is a separation between how the traffic is defined and how to control/activate it. The API/Console/GUI can load a traffic profile and start/stop/get a statistic. Due to this separation it is possible to share traffic profiles.

Table 4. Traffic profile formats
Profile Type Format Description

Native

Python

Most flexibile. Any format can be converted to native using the stl-sim command with the --native option.

HLT

Python

Uses HLT arguments.

YAML

YAML

The common denominator traffic profile. Information is shared between console, GUI, and simulator in YAML format. This format is difficult to use for defining packets; primarily for machine use. YAML can be converted to native using the stl-sim command with the --native option.

2.7.8. Tutorial: Port Layer Mode Configuration

Goal

Configure TRex port with either IPv4 or MAC addrees

TRex ports can operate in two different mutual exclusive modes:

  • Layer 2 mode - MAC level configuration

  • Layer 3 mode - IPv4/IPv6 configuration

When configuring a port for L2 mode, it is only required to provide the destination MAC address for the port (Legacy mode previous to v2.12 version).

When configuring a port for L3, it is required to provide both source IPv4/IPv6 address and a IPv4/IPv6 destination address.

As an intergral part of configuring L3, the client will try to ARP resolve the destination address and automatically configure the correct destination MAC. (instead of sending ARP request when starting traffic)

Note While in L3 mode, TRex server will generate gratuitous ARP packets to make sure that no ARP timeout on the DUT/router will result in a faliure of the test.
Example of configuring L2 mode Console

trex>service

trex>l2 --help
usage: port [-h] --port PORT --dst DST_MAC

Configures a port in L2 mode

optional arguments:
  -h, --help            show this help message and exit
  --port PORT, -p PORT  source port for the action
  --dst DST_MAC         Configure destination MAC address


trex(service)>l2 -p 0 --dst 6A:A7:B5:3A:4E:FF

Setting port 0 in L2 mode:                                   [SUCCESS]

trex>service --off
Example of configuring L2 mode- Python API
  client.set_service_mode(port = 0, enabled = True)

  client.set_l2_mode(port = 0, dst_mac = "6A:A7:B5:3A:4E:FF")

  client.set_service_mode(port = 0, enabled = False)
Example of configuring L3 mode- Console

trex>service


trex(service)>l3 --help
usage: port [-h] --port PORT --src SRC_IPV4 --dst DST_IPV4

Configures a port in L3 mode

optional arguments:
  -h, --help            show this help message and exit
  --port PORT, -p PORT  source port for the action
  --src SRC_IPV4        Configure source IPv4 address
  --dst DST_IPV4        Configure destination IPv4 address

trex(service)>l3 -p 0 --src 1.1.1.2 --dst 1.1.1.1

Setting port 0 in L3 mode:                                   [SUCCESS]


ARP resolving address '1.1.1.1':                             [SUCCESS]

trex>service --off
Example of configuring L3 mode - Python API

client.set_service_mode(port = 0, enabled = True)

client.set_l3_mode(port = 0, src_ipv4 = '1.1.1.2', dst_ipv4 = '1.1.1.1')

client.set_service_mode(port = 0, enabled = False)

2.8. Port Service Mode

In normal operation mode, to preserve high speed processing of packets, TRex ignores most of the RX traffic, with the exception of counting/statistic and handling latency flows.

In the following diagram it is illustrated how RX packets are handled. Only a portion is forwarded to the RX handling module and none of forward back to the Python client.

Figure 7. Port Under Normal Mode

We provide another mode called service mode in which a port will respond to ping, ARP requests and also provide a capabality in this mode to forward packets to the Python control plane for applying full duplex protocols (DCHP, IPv6 neighboring and etc.)

The following diagram illustrates of packets can be forwarded back to the Python client

Figure 8. Port Under Service Mode

In this mode, it is possible to write python plugins for emulation (e.g. IPV6 ND/DHCP) to prepare the setup and then move to normal mode for high speed testing

Example Of Switcing Between Service And Normal Mode


trex(service)>service --help
usage: service [-h] [--port PORTS [PORTS ...] | -a] [--off]

Configures port for service mode. In service mode ports will reply to ARP,
PING and etc.

optional arguments:
  -h, --help            show this help message and exit
  --port PORTS [PORTS ...], -p PORTS [PORTS ...]
                        A list of ports on which to apply the command
  -a                    Set this flag to apply the command on all available
                        ports
  --off                 Deactivates services on port(s)


trex>service

Enabling service mode on port(s) [0, 1]:                     [SUCCESS]

trex(service)>service --off

Disabling service mode on port(s) [0, 1]:                    [SUCCESS]
Example Of Switcing Between Service And Normal Mode-API

  client.set_service_mode(ports = [0, 1], enabled = True)

  client.set_service_mode(ports = [0, 1], enabled = False)

2.8.1. ARP / ICMP response

Important Only while in service mode, ports will reply to ICMP echo requests and ARP requests.

2.8.2. Packet Capturing

Important The following section is available only under service mode

While under service mode TRex provides couple of ways to examine and manipulate both RX and TX packets.

Packet capturing is implemented by allocating one more more fast in-memory queues on the server side that will copy-and-store the packet buffer.

Each queue can be defined with storage, which ports on either TX/RX it should capture and whether it should be cyclic or fixed

Figure 9. Packet Captruing Architecture

The above architecture implies that we can capture at high speed for a short amount of time.

For example, A queue of 1 million packets can be allocated as a cyclic queue and be active under a rate of couple of Mpps. It effectively provide a sampling of the last 1 million packets seen by the server with the given filters.

2.8.3. Packet Capturing API Usage

The Python API usages is fairly simple:

Python API:

# move port 1 to service mode as we want to capture traffic on it
client.set_service_mode(ports = 1)

# start a capture on port 1 RX side with a limit and a mode
capture = client.start_capture(rx_ports = 1, limit = 100, mode = 'fixed')

# execute your code here

# save the packets to a file or to a list (see the Python API docs)
client.stop_capture(capture['id'], '/home/mydir/port_0_rx.pcap')

# exit service mode on port 1
client.set_service_mode(ports = 1, enabled = False)

2.8.4. Packet Capturing Console Usage

The console provides couple of flexible ways to handle packet capturing

  • Capture Monitoring

  • Capture Recording

Capture Monitoring

Capture monitoring is a non-persistent way to capture and show packets from either TX / RX of one or more ports

Monitoring provides 3 modes:

  • Low Verbose - short line per packet will be displayed

  • High Verbose - a full scapy show will be displayed per packet

  • Wireshark Pipe - launches Wireshark with a pipe connected to the traffic being captured

In the first two options, packets information will be displayed on the console

So for high amount of traffic being monitored, consider Wireshark Pipe or the Capture Recording

Example of capturing traffic using the console with verbose on
trex>service                                                                  1

Enabling service mode on port(s) [0, 1, 2, 3]:               [SUCCESS]

trex(service)>capture monitor start --rx 3 -v                                 2

Starting stdout capture monitor - verbose: 'high'            [SUCCESS]


*** use 'capture monitor stop' to abort capturing... ***

trex(service)>arp -p 3                                                        3

Resolving destination on port(s) [3]:                        [SUCCESS]

Port 3 - Recieved ARP reply from: 1.1.1.1, hw: 90:e2:ba:ae:88:b8              4
38.14 [ms]

trex(service)>

#1 Port: 3 ?-- RX

    Type: ARP, Size: 60 B, TS: 16.98 [sec]

    ###[ Ethernet ]###
      dst       = 90:e2:ba:af:13:89
      src       = 90:e2:ba:ae:88:b8
      type      = 0x806
    ###[ ARP ]###
         hwtype    = 0x1
         ptype     = 0x800
         hwlen     = 6
         plen      = 4
         op        = is-at                                                    5
         hwsrc     = 90:e2:ba:ae:88:b8
         psrc      = 1.1.1.1
         hwdst     = 90:e2:ba:af:13:89
         pdst      = 4.4.4.4
    ###[ Padding ]###
            load      = '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'


trex(service)>
1 Move to service mode to allow capturing
2 Activate a capture monitor on port 3 RX side with verbose on
3 Send an ARP request on port 3
4 The console shows the returning packet
5 is-at ARP response was captured
Example of capturing traffic using Wireshark pipe
trex(service)>capture monitor start --rx 3 -p                                 1

Starting pipe capture monitor                                [SUCCESS]


Trying to locate Wireshark                                   [SUCCESS]


Checking permissions on '/usr/bin/dumpcap'                   [SUCCESS]


Launching '/usr/bin/wireshark -k -i /tmp/tmputa4jf3c'        [SUCCESS]        2


Waiting for Wireshark pipe connection                        [SUCCESS]        3


*** Capture monitoring started ***                                            4

trex(service)>arp                                                             5

Resolving destination on port(s) [0, 1, 2, 3]:               [SUCCESS]

Port 0 - Recieved ARP reply from: 4.4.4.4, hw: 90:e2:ba:af:13:89
Port 1 - Recieved ARP reply from: 3.3.3.3, hw: 90:e2:ba:af:13:88
Port 2 - Recieved ARP reply from: 2.2.2.2, hw: 90:e2:ba:ae:88:b9
Port 3 - Recieved ARP reply from: 1.1.1.1, hw: 90:e2:ba:ae:88:b8
1 Activate a monitor using a Wireshark pipe
2 Try to automatically launch Wireshark with connection the pipe
3 Console will block until connection was established
4 Monitor is active
5 Send ARP request
Figure 10. Wireshark Pipe
Capture Recording

In addition to monitoring, the console allows a simple recording as well.

Recording allows the user to define a fixed size queue which then can be saved to a PCAP file.

Example of capturing a traffic to a fixed size queue
trex(service)>capture record start --rx 3 --limit 200                         1

Starting packet capturing up to 200 packets                  [SUCCESS]

*** Capturing ID is set to '4' ***                                            2
*** Please call 'capture record stop --id 4 -o <out.pcap>' when done ***

trex(service)>capture                                                         3

Active Recorders

      ID        |     Status      |     Packets     |      Bytes      |    TX Ports     |    RX Ports
 ------------------------------------------------------------------------------------------------------
       4        |     ACTIVE      |     [0/200]     |       0 B       |        -        |        3



trex(service)>start -f stl/imix.py -m 1kpps -p 0 --force                      4

Removing all streams from port(s) [0]:                       [SUCCESS]


Attaching 3 streams to port(s) [0]:                          [SUCCESS]


Starting traffic on port(s) [0]:                             [SUCCESS]

20.42 [ms]

trex(service)>capture                                                         5

Active Recorders

      ID        |     Status      |     Packets     |      Bytes      |    TX Ports     |    RX Ports
 ------------------------------------------------------------------------------------------------------
       4        |     ACTIVE      |    [200/200]    |    74.62 KB     |        -        |        3


trex(service)>capture record stop --id 4 -o /tmp/rx_3.pcap                    6

Stopping packet capture 4                                    [SUCCESS]


Writing 200 packets to '/tmp/rx_3.pcap'                      [SUCCESS]


Removing PCAP capture 4 from server                          [SUCCESS]

trex(service)>
1 Start a packet record on port 3 RX side with a limit of 200 packets
2 A new capture was created with an ID 4
3 Showing the capture status - currently empty
4 Start traffic on port 0 which is connected to port 3
5 Showing the capture status - full
6 Save 200 packets to an output file called /tmp/rx_3.pcap

2.8.5. Packet Capturing Video Tutorials

The Tutorial shows a little bit of our new packet capture ability

2.9. Neighboring Protocols

As mentioned, in order to preserve high speed traffic generation, TRex handles neighboring protocols in pre test phase.

A test that requires running a neighboring protocol should first move to service mode, execute the required steps in Python, switch back to normal mode and start the actual test.

2.9.1. ARP

A basic neighboring protocol that is provided as part of TRex is ARP.

For example, let’s take a look at the following setup:

Figure 11. Router ARP

trex>service                                                                   #1

Enabling service mode on port(s) [0, 1]:                     [SUCCESS]

trex(service)>portattr  --port 0

             port       |          0           |
        ------------------------------------------
        driver          |    rte_ixgbe_pmd     |
        description     |  82599EB 10-Gigabit  |
        link status     |          UP          |
        link speed      |       10 Gb/s        |
        port status     |         IDLE         |
        promiscuous     |         off          |
        flow ctrl       |         none         |
        --              |                      |
        src IPv4        |          -           |
        src MAC         |  00:00:00:01:00:00   |
        ---             |                      |
        Destination     |  00:00:00:01:00:00   |
        ARP Resolution  |          -           |
        ----            |                      |
        PCI Address     |     0000:03:00.0     |
        NUMA Node       |          0           |
        -----           |                      |
        RX Filter Mode  |    hardware match    |
        RX Queueing     |         off          |
        RX sniffer      |         off          |
        Grat ARP        |         off          |


trex(service)>l3 -p -s 1.1.1.1 -d 1.1.1.2                         #2

trex(service)>arp -p 0 1                                          #3

Resolving destination on port(s) [0, 1]:                     [SUCCESS]


Port 0 - Recieved ARP reply from: 1.1.1.1, hw: d0:d0:fd:a8:a1:01
Port 1 - Recieved ARP reply from: 1.1.2.1, hw: d0:d0:fd:a8:a1:02

trex(service)>service --off                                       #4
1 Enable service mode
2 Set IPv4/default gateway. it will resolve the arp
3 repeat ARP resolution
4 exist from service mode

to revert back to MAC address mode (without ARP resolution) you do the following

Disable L3 mode

trex>l2 -p 0 --dst 00:00:00:01:00:00       #1

trex>portattr  --port 0

             port       |          0           |
        ------------------------------------------
        driver          |    rte_ixgbe_pmd     |
        description     |  82599EB 10-Gigabit  |
        link status     |          UP          |
        link speed      |       10 Gb/s        |
        port status     |         IDLE         |
        promiscuous     |         off          |
        flow ctrl       |         none         |
        --              |                      |
        src IPv4        |          -           |
        src MAC         |  00:00:00:01:00:00   |
        ---             |                      |
        Destination     |  00:00:00:01:00:00   |
        ARP Resolution  |          -           |
        ----            |                      |
        PCI Address     |     0000:03:00.0     |
        NUMA Node       |          0           |
        -----           |                      |
        RX Filter Mode  |    hardware match    |
        RX Queueing     |         off          |
        RX sniffer      |         off          |
        Grat ARP        |         off          |
1 disable service mode
Python API:

client.set_service_mode(ports = [0, 1], enabled = True)                  1

# configure port 0, 1 to Layer 3 mode
client.set_l3_mode(port = 0, src_ipv4 = '1.1.1.2', dst_ipv4 = '1.1.1.2') 2
client.set_l3_mode(port = 1, src_ipv4 = '1.1.2.2', dst_ipv4 = '1.1.2.1')

# ARP resolve ports 0, 1
c.resolve(ports = [0, 1])

client.set_service_mode(ports = [0, 1], enabled = False)                 3
1 Enable service mode
2 configure IPv4 and Default Gateway
3 Disable service mode

2.9.2. ICMP

Another basic protocol provided with TRex is ICMP. It is possible, under service mode to ping the DUT or even a TRex port from the console / API.

TRex Console

trex(service)>ping --help
usage: ping [-h] --port PORT -d PING_IPV4 [-s PKT_SIZE] [-n COUNT]

pings the server / specific IP

optional arguments:
  -h, --help            show this help message and exit
  --port PORT, -p PORT  source port for the action
  -d PING_IPV4          which IPv4 to ping
  -s PKT_SIZE           packet size to use
  -n COUNT, --count COUNT
                        How many times to ping [default is 5]

trex(service)>ping -p 0 -d 1.1.2.2

Pinging 1.1.2.2 from port 0 with 64 bytes of data:
Reply from 1.1.2.2: bytes=64, time=27.72ms, TTL=127
Reply from 1.1.2.2: bytes=64, time=1.40ms, TTL=127
Reply from 1.1.2.2: bytes=64, time=1.31ms, TTL=127
Reply from 1.1.2.2: bytes=64, time=1.78ms, TTL=127
Reply from 1.1.2.2: bytes=64, time=1.95ms, TTL=127
Python API

# move to service mode
client.set_service_mode(ports = ports, enabled = True)

# configure port 0, 1 to Layer 3 mode
client.set_l3_mode(port = 0, src_ipv4 = '1.1.1.2', dst_ipv4 = '1.1.1.1')
client.set_l3_mode(port = 1, src_ipv4 = '1.1.2.2', dst_ipv4 = '1.1.2.1')

# ping port 1 from port 0 through the router
client.ping_ip(src_port = 0, dst_ipv4 = '1.1.2.2', pkt_size = 64)        1

# disable service mode
client.set_service_mode(enabled = False)
1 Check connectivity

2.9.3. IPv6 ND client

At this phase, implemented scanning of network for IPv6 enabled neighbors and ping nearby devices from the console.
Next phase, planned support at the CPP server.
The advantage of those methods is that they can be easily extended to simulate lots of clients in automation.

Scanning example:

Ping example:

Those utilities (available from API as well) can help user to configure next hop. From the console, one could set "l2" destination MAC taken from the scan6 result:

For setting own IPv6, we use local address as described in RFC 3513.
For scanning of network, we ping the multicast address ff02::1 and establish connection via NS/ND conversations.

Additional links on scanning network:

Example of using IPv6 methods in automation:

2.10. Traffic profile Tutorials

2.10.1. Tutorial: Simple Interleaving streams

Goal

Demonstrate interleaving of multiple streams.

The following example demonstrates 3 streams with different rates (10, 20, 40 PPS) and different start times, based on an inter-stream gap (ISG) of 0, 25 msec, or 50 msec.

Interleaving multiple streams
    def create_stream (self):

        # create a base packet and pad it to size
        size = self.fsize - 4 # no FCS
        base_pkt =  Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)       1
        base_pkt1 =  Ether()/IP(src="16.0.0.2",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt2 =  Ether()/IP(src="16.0.0.3",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        pad = max(0, size - len(base_pkt)) * 'x'


        return STLProfile( [ STLStream( isg = 0.0,
                                        packet = STLPktBuilder(pkt = base_pkt/pad),
                                        mode = STLTXCont( pps = 10),                         2
                                        ),

                             STLStream( isg = 25000.0, #defined in usec, 25 msec
                                        packet  = STLPktBuilder(pkt = base_pkt1/pad),
                                        mode    = STLTXCont( pps = 20),                      3
                                        ),

                             STLStream(  isg = 50000.0,#defined in usec, 50 msec
                                         packet = STLPktBuilder(pkt = base_pkt2/pad),
                                         mode    = STLTXCont( pps = 40)                      4

                                        )
                            ]).get_streams()
1 Defines template packets using Scapy.
2 Defines streams with rate of 10 PPS.
3 Defines streams with rate of 20 PPS.
4 Defines streams with rate of 40 PPS.
Output

The folowing figure presents the output.

Figure 12. Interleaving of streams
Discussion
  • Stream #1

    • Schedules a packet each 100 msec

  • Stream #2

    • Schedules a packet each 50 msec

    • Starts 25 msec after stream #1

  • Stream #3

    • Schedules a packet each 25 msec

    • Starts 50 msec after stream #1

You can run the traffic profile in the TRex simulator and view the details in the pcap file containing the simulation output.

[bash]>./stl-sim -f stl/simple_3pkt.py -o b.pcap -l 200

To run the traffic profile from console in TRex, use the following command.

trex>start -f stl/simple_3pkt.py -m 10mbps -a

2.10.2. Tutorial: Multi burst streams - action next stream

Goal

Create a profile with a stream that trigger another stream

The following example demonstrates:

  1. More than one stream

  2. Burst of 10 packets

  3. One stream activating another stream (see self_start=False in the traffic profile)

    def create_stream (self):

        # create a base packet and pad it to size
        size = self.fsize - 4 # no FCS
        base_pkt =  Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt1 =  Ether()/IP(src="16.0.0.2",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt2 =  Ether()/IP(src="16.0.0.3",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        pad = max(0, size - len(base_pkt)) * 'x'


        return STLProfile( [ STLStream( isg = 10.0, # star in delay
                                        name    ='S0',
                                        packet = STLPktBuilder(pkt = base_pkt/pad),
                                        mode = STLTXSingleBurst( pps = 10, total_pkts = 10),  1
                                        next = 'S1'), # point to next stream

                             STLStream( self_start = False, # stream is  disabled enable trow S0  2
                                        name    ='S1',
                                        packet  = STLPktBuilder(pkt = base_pkt1/pad),
                                        mode    = STLTXSingleBurst( pps = 10, total_pkts = 20),
                                        next    = 'S2' ),

                             STLStream(  self_start = False, # stream is  disabled enable trow S0 3
                                         name   ='S2',
                                         packet = STLPktBuilder(pkt = base_pkt2/pad),
                                         mode = STLTXSingleBurst( pps = 10, total_pkts = 30 )
                                        )
                            ]).get_streams()
1 Stream S0 is configured to self_start=True, starts after 10 sec.
2 S1 is configured to self_start=False, activated by stream S0.
3 S2 is activated by S1.

To run the simulation, use this command.

[bash]>./stl-sim -f stl/stl/burst_3pkt_60pkt.py -o b.pcap

The generated pcap file has 60 packets. The first 10 packets have src_ip=16.0.0.1. The next 20 packets has src_ip=16.0.0.2. The next 30 packets has src_ip=16.0.0.3.

This run the profile from console use this command.

TRex>start -f stl/stl/burst_3pkt_60pkt.py --port 0

2.10.3. Tutorial: Multi-burst mode

Goal : Use Multi-burst transmit mode


    def create_stream (self):

        # create a base packet and pad it to size
        size = self.fsize - 4 # no FCS
        base_pkt =  Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt1 =  Ether()/IP(src="16.0.0.2",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        pad = max(0, size - len(base_pkt)) * 'x'


        return STLProfile( [ STLStream( isg = 10.0, # start in delay                                  1
                                        name    ='S0',
                                        packet = STLPktBuilder(pkt = base_pkt/pad),
                                        mode = STLTXSingleBurst( pps = 10, total_pkts = 10),
                                        next = 'S1'), # point to next stream

                             STLStream( self_start = False, # stream is disabled. Enabled by S0       2
                                        name    ='S1',
                                        packet  = STLPktBuilder(pkt = base_pkt1/pad),
                                        mode    = STLTXMultiBurst( pps = 1000,
                                                                   pkts_per_burst = 4,
                                                                   ibg = 1000000.0,
                                                                   count = 5)
                                        )

                            ]).get_streams()
1 Stream S0 waits 10 usec (inter-stream gap, ISG) and then sends a burst of 10 packets at 10 PPS.
2 Multi-burst of 5 bursts of 4 packets with an inter-burst gap of 1 second.

The following illustration does not fully match the Python example cited above. It has been simplified, such as using a 0.5 second ISG, for illustration purposes.

Figure 13. Example of multiple streams

2.10.4. Tutorial: Loops of streams

Goal : Demonstrate a limited loop of streams

    def create_stream (self):

        # create a base packet and pad it to size
        size = self.fsize - 4 # no FCS
        base_pkt =  Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt1 =  Ether()/IP(src="16.0.0.2",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        base_pkt2 =  Ether()/IP(src="16.0.0.3",dst="48.0.0.1")/UDP(dport=12,sport=1025)
        pad = max(0, size - len(base_pkt)) * 'x'


        return STLProfile( [ STLStream( isg = 10.0, # start in delay
                                        name    ='S0',
                                        packet = STLPktBuilder(pkt = base_pkt/pad),
                                        mode = STLTXSingleBurst( pps = 10, total_pkts = 1),
                                        next = 'S1'), # point to next stream

                             STLStream( self_start = False, # stream is disabled. Enabled by S0
                                        name    ='S1',
                                        packet  = STLPktBuilder(pkt = base_pkt1/pad),
                                        mode    = STLTXSingleBurst( pps = 10, total_pkts = 2),
                                        next    = 'S2' ),

                             STLStream(  self_start = False, # stream is disabled. Enabled by S1
                                         name   ='S2',
                                         packet = STLPktBuilder(pkt = base_pkt2/pad),
                                         mode = STLTXSingleBurst( pps = 10, total_pkts = 3 ),
                                         action_count = 2, # loop 2 times                       1
                                         next    = 'S0' # loop back to S0
                                        )
                            ]).get_streams()
1 go back to S0 but limit it to 2 loops

2.10.5. Tutorial: IMIX with UDP packets, bi-directional

Goal : Demonstrate how to create an IMIX traffic profile.

This profile defines 3 streams, with packets of different sizes. The rate is different for each stream/size. See the Wikipedia article on Internet Mix.

    def __init__ (self):
        # default IP range
        self.ip_range = {'src': {'start': "10.0.0.1", 'end': "10.0.0.254"},
                         'dst': {'start': "8.0.0.1",  'end': "8.0.0.254"}}

        # default IMIX properties
        self.imix_table = [ {'size': 60,   'pps': 28,  'isg':0 },
                            {'size': 590,  'pps': 16,  'isg':0.1 },
                            {'size': 1514, 'pps': 4,   'isg':0.2 } ]


    def create_stream (self, size, pps, isg, vm ):
        # create a base packet and pad it to size
        base_pkt = Ether()/IP()/UDP()
        pad = max(0, size - len(base_pkt)) * 'x'

        pkt = STLPktBuilder(pkt = base_pkt/pad,
                            vm = vm)

        return STLStream(isg = isg,
                         packet = pkt,
                         mode = STLTXCont(pps = pps))


    def get_streams (self, direction = 0, **kwargs):                            1

        if direction == 0:                                                      2
            src = self.ip_range['src']
            dst = self.ip_range['dst']
        else:
            src = self.ip_range['dst']
            dst = self.ip_range['src']

        # construct the base packet for the profile

        vm =[                                                                   3
            # src
            STLVmFlowVar(name="src",
                         min_value=src['start'],
                         max_value=src['end'],
                         size=4,op="inc"),
            STLVmWrFlowVar(fv_name="src",pkt_offset= "IP.src"),

            # dst
            STLVmFlowVar(name="dst",
                         min_value=dst['start'],
                         max_value=dst['end'],
                         size=4,
                         op="inc"),
            STLVmWrFlowVar(fv_name="dst",pkt_offset= "IP.dst"),

            # checksum
            STLVmFixIpv4(offset = "IP")

            ]

        # create imix streams
        return [self.create_stream(x['size'], x['pps'],x['isg'] , vm) for x in self.imix_table]
1 Constructs a diffrent stream for each direction (replaces src and dest).
2 Even port id has direction==0 and odd has direction==1.
3 Field Engine program to change fields within the packets.

2.10.6. Tutorial: Field Engine, Syn attack

The following example demonstrates changing packet fields. The Field Engine (FE) has a limited number of instructions/operation, which support most use cases.

The FE can
  • Allocate stream variables in a stream context

  • Write a stream variable to a packet offset

  • Change packet size

  • and more…

  • There is a plan to add LuaJIT to be more flexible at the cost of performance.

Examples:
  • Change ipv4.tos value (1 to 10)

  • Change packet size to a random value in the range 64 to 9K

  • Create a range of flows (change src_ip, dest_ip, src_port, dest_port)

  • Update the IPv4 checksum

For more information, see here

The following example demonstrates creating a SYN attack from many src addresses to one server.

    def create_stream (self):

        # TCP SYN
        base_pkt  = Ether()/IP(dst="48.0.0.1")/TCP(dport=80,flags="S")      1


        # vm
        vm = STLScVmRaw( [ STLVmFlowVar(name="ip_src",
                                              min_value="16.0.0.0",
                                              max_value="18.0.0.254",
                                              size=4, op="random"),         2

                           STLVmFlowVar(name="src_port",
                                              min_value=1025,
                                              max_value=65000,
                                              size=2, op="random"),         3

                           STLVmWrFlowVar(fv_name="ip_src", pkt_offset= "IP.src" ), 4

                           STLVmFixIpv4(offset = "IP"), # fix checksum              5

                           STLVmWrFlowVar(fv_name="src_port",                       6
                                                pkt_offset= "TCP.sport") # U

                          ]
                       )

        pkt = STLPktBuilder(pkt = base_pkt,
                            vm = vm)

        return STLStream(packet = pkt,
                         random_seed = 0x1234,# can be removed. will give the same random value any run
                         mode = STLTXCont())
1 Creates SYN packet using Scapy .
2 Defines a stream variable name=ip_src, size 4 bytes, for IPv4.
3 Defines a stream variable name=src_port, size 2 bytes, for port.
4 Writes ip_src stream var into IP.src packet offset. Scapy calculates the offset. Can specify IP:1.src for a second IP header in the packet.
5 Fixes IPv4 checksum. Provides the header name IP. Can specify IP:1 for a second IP.
6 Writes src_port stream var into TCP.sport packet offset. TCP checksum is not updated here.
Warning Original Scapy cannot calculate offset for a header/field by name. This offset capability will not work for all cases. In some complex cases, Scapy may rebuild the header. In such cases, specify the offset as a number.

Output pcap file:

Table 5. Output - pcap file
pkt Client IPv4 Client Port

1

17.152.71.218

5814

2

17.7.6.30

26810

3

17.3.32.200

1810

4

17.135.236.168

55810

5

17.46.240.12

1078

6

16.133.91.247

2323

2.10.7. Tutorial: Field Engine, Tuple Generator

The following example creates multiple flows from the same packet template. The Tuple Generator instructions are used to create two stream variables for IP and port. See here

        base_pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)

        pad = max(0, size - len(base_pkt)) * 'x'

        vm = STLScVmRaw( [   STLVmTupleGen ( ip_min="16.0.0.1",                              1
                                             ip_max="16.0.0.2",
                                             port_min=1025,
                                             port_max=65535,
                                             name="tuple"), # define tuple gen

                             STLVmWrFlowVar (fv_name="tuple.ip", pkt_offset= "IP.src" ),     2
                             STLVmFixIpv4(offset = "IP"),
                             STLVmWrFlowVar (fv_name="tuple.port", pkt_offset= "UDP.sport" ) 3
                                  ]
                              )

        pkt = STLPktBuilder(pkt = base_pkt/pad,
                            vm = vm)
1 Defines a struct with two dependent variables: tuple.ip, tuple.port
2 Writes the tuple.ip variable to IPv4.src field offset.
3 Writes the tuple.port variable to UDP.sport field offset. Set UDP.checksum to 0.
Table 6. Output - pcap file
pkt Client IPv4 Client Port

1

16.0.0.1

1025

2

16.0.0.2

1025

3

16.0.0.1

1026

4

16.0.0.2

1026

5

16.0.0.1

1027

6

16.0.0.2

1027

  • Number of clients: 2: 16.0.0.1 and 16.0.0.2

  • Number of flows is limited to 129020: (2 * (65535-1025))

  • The stream variable size should match the size of the FlowVarWr instruction.

2.10.8. Tutorial: Field Engine, write to a bit-field packet

The following example writes a stream variable to a bit field packet variable. In this example, an MPLS label field is changed.

Table 7. MPLS header

Label

TC

S

TTL

0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

8

9

0

1


    def create_stream (self):
        # 2 MPLS label the internal with  s=1 (last one)
        pkt =  Ether()/
               MPLS(label=17,cos=1,s=0,ttl=255)/
               MPLS(label=0,cos=1,s=1,ttl=12)/
               IP(src="16.0.0.1",dst="48.0.0.1")/
               UDP(dport=12,sport=1025)/('x'*20)

        vm = STLScVmRaw( [ STLVmFlowVar(name="mlabel",                                 1
                                        min_value=1,
                                        max_value=2000,
                                        size=2, op="inc"), # 2 bytes var               2
                           STLVmWrMaskFlowVar(fv_name="mlabel",
                                              pkt_offset= "MPLS:1.label",              3
                                              pkt_cast_size=4,
                                              mask=0xFFFFF000,shift=12) # write to 20bit MSB
                          ]
                       )

        # burst of 100 packets
        return STLStream(packet = STLPktBuilder(pkt = pkt ,vm = vm),
                         mode = STLTXSingleBurst( pps = 1, total_pkts = 100) )
1 Defines a variable size of 2 bytes.
2 Writes the stream variable label with a shift of 12 bits, with a 20-bit MSB mask. Cast the stream variables of 2 bytes to 4 bytes.
3 Change the second MPLS header.

2.10.9. Tutorial: Field Engine, Random packet size

The following example demonstrates varies the packet size randomly, as follows:

  1. Defines the template packet with maximum size.

  2. Trims the packet to the size you want.

  3. Updates the packet fields according to the new size.


    def create_stream (self):
        # pkt
        p_l2  = Ether()
        p_l3  = IP(src="16.0.0.1",dst="48.0.0.1")
        p_l4  = UDP(dport=12,sport=1025)
        pyld_size = max(0, self.max_pkt_size_l3 - len(p_l3/p_l4))
        base_pkt = p_l2/p_l3/p_l4/('\x55'*(pyld_size))

        l3_len_fix =-(len(p_l2))
        l4_len_fix =-(len(p_l2/p_l3))


        # vm
        vm = STLScVmRaw( [ STLVmFlowVar(name="fv_rand",                            1
                                        min_value=64,
                                        max_value=len(base_pkt),
                                        size=2,
                                        op="random"),

                           STLVmTrimPktSize("fv_rand"), # total packet size        2

                           STLVmWrFlowVar(fv_name="fv_rand",                       3
                                          pkt_offset= "IP.len",
                                          add_val=l3_len_fix), # fix ip len

                           STLVmFixIpv4(offset = "IP"),

                           STLVmWrFlowVar(fv_name="fv_rand",                       4
                                          pkt_offset= "UDP.len",
                                          add_val=l4_len_fix) # fix udp len
                          ]
                       )
1 Defines a random stream variable with the maximum size of the packet.
2 Trims the packet size to the fv_rand value.
3 Fixes ip.len to reflect the packet size.
4 Fixes udp.len to reflect the packet size.

2.10.10. Tutorial: Field Engine, Significantly improve performance

The following example demonstrates a way to significantly improve Field Engine performance in case it is needed.

Field Engine has a cost of CPU instructions and CPU memory bandwidth. There is a way to significantly improve performance by caching the packets and run the Field Engine offline(before sending the packets). The limitation is that you can have only a limited number of packets that can be cached (order or 10K depends how much memory you have). For example a program that change the src_ip to a random value can’t be utilized this technique and still have random src_ip. Usually this is done with small packets (64bytes) where performance is an issue. This method can improve long packets senario with a complex Field Engine program.


    def create_stream (self):
        # create a base packet and pad it to size
        size = self.fsize - 4; # no FCS

        base_pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)

        pad = max(0, size - len(base_pkt)) * 'x'

        vm = STLScVmRaw( [   STLVmFlowVar ( "ip_src",
                                            min_value="10.0.0.1",
                                            max_value="10.0.0.255",
                                            size=4, step=1,op="inc"),

                             STLVmWrFlowVar (fv_name="ip_src",
                                             pkt_offset= "IP.src" ),

                             STLVmFixIpv4(offset = "IP")
                         ],
                         split_by_field = "ip_src",
                         cache_size =255 # the cache size             1
                        );

        pkt = STLPktBuilder(pkt = base_pkt/pad,
                            vm = vm)

        stream = STLStream(packet = pkt,
                         mode = STLTXCont())
        return stream
1 Cache 255 packets. The range is the same as ip_src stream variable

This FE program will run x2-5 faster compared to native (without cache). In this specific example the output will be exactly the same.

Again the limitations of this method are:

  1. The total number of cache packets for all the streams all the ports in limited by the memory pool (range of ~10-40K)

  2. There could be cases that the cache options won’t be exactly the same as the normal program, for example, in case of a program that step in prime numbers or with a random variable

2.10.11. Tutorial: New Scapy header

The following example uses a header that is not supported by Scapy by default. The example demonstrates VXLAN support.


# Adding header that does not exists yet in Scapy
# This was taken from pull request of Scapy
#


# RFC 7348 - Virtual eXtensible Local Area Network (VXLAN):                                     1
# A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks
# http://tools.ietf.org/html/rfc7348
_VXLAN_FLAGS = ['R' for i in range(0, 24)] + ['R', 'R', 'R', 'I', 'R', 'R', 'R', 'R', 'R']

class VXLAN(Packet):
    name = "VXLAN"
    fields_desc = [FlagsField("flags", 0x08000000, 32, _VXLAN_FLAGS),
                   ThreeBytesField("vni", 0),
                   XByteField("reserved", 0x00)]

    def mysummary(self):
        return self.sprintf("VXLAN (vni=%VXLAN.vni%)")

bind_layers(UDP, VXLAN, dport=4789)
bind_layers(VXLAN, Ether)


class STLS1(object):

    def __init__ (self):
        pass

    def create_stream (self):
        pkt =  Ether()/IP()/UDP(sport=1337,dport=4789)/VXLAN(vni=42)/Ether()/IP()/('x'*20)    2
        #pkt.show2()
        #hexdump(pkt)

        # burst of 17 packets
        return STLStream(packet = STLPktBuilder(pkt = pkt ,vm = []),
                         mode = STLTXSingleBurst( pps = 1, total_pkts = 17) )

1 Downloads and adds a Scapy header from the specified location. Alternatively, write a Scapy header.
2 Apply the header.

For more information how to define headers see Adding new protocols in the Scapy documentation.

2.10.12. Tutorial: Field Engine, Multiple Clients

The following example generates traffic from many clients with different IP/MAC addresses to one server.

Figure 14. Multiple clients to single server
  1. Send a gratuitous ARP from B→D with server IP/MAC (58.55.1.1).

  2. DUT learns the ARP of server IP/MAC (58.55.1.1).

  3. Send traffic from A→C with many client IP/MAC addresses.

Example:

Base source IPv4 : 55.55.1.1 Destination IPv4: 58.55.1.1

Increment src ipt portion starting at 55.55.1.1 for n number of clients (55.55.1.1, 55.55.1.2) Src MAC: start with 0000.dddd.0001, increment mac in steps of 1 Dst MAC: Fixed - 58.55.1.1

The following sends a gratuitous ARP from the TRex server port for this server (58.0.0.1).

    def create_stream (self):
        # create a base packet and pad it to size
        base_pkt =  Ether(src="00:00:dd:dd:01:01",
                          dst="ff:ff:ff:ff:ff:ff")/
                    ARP(psrc="58.55.1.1",
                        hwsrc="00:00:dd:dd:01:01",
                        hwdst="00:00:dd:dd:01:01",
                        pdst="58.55.1.1")

Then traffic can be sent from client side: A→C

class STLS1(object):

    def __init__ (self):
        self.num_clients  =30000 # max is 16bit
        self.fsize        =64

    def create_stream (self):

        # create a base packet and pad it to size
        size = self.fsize - 4 # no FCS
        base_pkt =  Ether(src="00:00:dd:dd:00:01")/
                          IP(src="55.55.1.1",dst="58.55.1.1")/UDP(dport=12,sport=1025)
        pad = max(0, size - len(base_pkt)) * 'x'

        vm = STLScVmRaw( [ STLVmFlowVar(name="mac_src",
                                        min_value=1,
                                        max_value=self.num_clients,
                                        size=2, op="inc"), # 1 byte varible, range 1-10

                           STLVmWrFlowVar(fv_name="mac_src", pkt_offset= 10),        1
                           STLVmWrFlowVar(fv_name="mac_src" ,
                                          pkt_offset="IP.src",
                                          offset_fixup=2),                           2
                           STLVmFixIpv4(offset = "IP")
                          ]
                         ,split_by_field = "mac_src"  # split
                       )

        return STLStream(packet = STLPktBuilder(pkt = base_pkt/pad,vm = vm),
                         mode = STLTXCont( pps=10 ))
1 Writes the stream variable mac_src with an offset of 10 (last 2 bytes of src_mac field). The offset is specified explicitly as 10 bytes from the beginning of the packet.
2 Writes the stream variable mac_src with an offset determined by the offset of IP.src plus the offset_fixup of 2.

2.10.13. Tutorial: Field Engine, many clients with ARP

In the following example, there are two Switchs SW1 and SW2. TRex port 0 is connected to SW1 and TRex port 1 is connected to SW2. There are 253 hosts connected to SW1 and SW2 with two network ports.

Table 8. Client side the network of the hosts
Name Description

TRex port 0 MAC

00:00:01:00:00:01

TRex port 0 IPv4

16.0.0.1

IPv4 host client side range

16.0.0.2-16.0.0.254

MAC host client side range

00:00:01:00:00:02-00:00:01:00:00:FE

Table 9. Server side the network of the hosts
Name Description

TRex port 1 MAC

00:00:02:00:00:01

TRex port 1 IPv4

48.0.0.1

IPv4 host server side range

48.0.0.2-48.0.0.254

MAC host server side range

00:00:02:00:00:02-00:00:02:00:00:FE

Figure 15. arp/nd

In the following example, there are two Switchs SW1 and SW2. TRex port 0 is connected to SW1 and TRex port 1 is connected to SW2 In this example, because there are many hosts connected to the same network using SW1 and not as a next hope, we would like to teach SW1 the MAC addresses of the hosts and not to send the traffic directly to the hosts MAC (as it is unknown) For that we would send an ARP to all the hosts (16.0.0.2-16.0.0.254) from TRex port 0 and gratuitous ARP from server side (48.0.0.1) TRex port 1 as the first stage of the test

So the step would be like that:

  1. Send a gratuitous ARP from TRex port 1 with server IP/MAC (48.0.0.1) after this stage SW2 will know that 48.0.0.1 is located after this port of SW2.

  2. Send ARP request for all hosts from port 0 with a range of 16.0.0.2-16.0.0.254 after this stage all switch ports will learn the PORT/MAC locations. Without this stage the first packets from TRex port 0 will be flooded to all Switch ports.

  3. send traffic from TRex0→clients, port 1→servers

ARP traffic profile

 base_pkt =  Ether(dst="ff:ff:ff:ff:ff:ff")/
            ARP(psrc="16.0.0.1",hwsrc="00:00:01:00:00:01", pdst="16.0.0.2")                      1

 vm = STLScVmRaw( [ STLVmFlowVar(name="mac_src", min_value=2, max_value=254, size=2, op="inc"),  2
                    STLVmWrFlowVar(fv_name="mac_src" ,pkt_offset="ARP.pdst",offset_fixup=2),
                  ]
                 ,split_by_field = "mac_src"  # split
                )

1 ARP packet with TRex port 0 MAC and IP and pdst as variable.
2 Write it to ARP.pdst.
Gratuitous ARP traffic profile

        base_pkt =  Ether(src="00:00:02:00:00:01",dst="ff:ff:ff:ff:ff:ff")/
                    ARP(psrc="48.0.0.1",hwsrc="00:00:02:00:00:01",
                        hwdst="00:00:02:00:00:01", pdst="48.0.0.1") 1
1 G ARP packet with TRex port 1 MAC and IP no need a VM.
Note

This principal can be done for IPv6 too. ARP could be replaced with Neighbor Solicitation IPv6 packet.

2.10.14. Tutorial: Field Engine, split to core

Post v2.08 version split to core directive was deprecated and was kept for backward compatibility. The new implementation is always to split as if the profile was sent from one core. The user of TRex is oblivious to the number of cores.

    def create_stream (self):

        # TCP SYN
        base_pkt  = Ether()/IP(dst="48.0.0.1")/TCP(dport=80,flags="S")


        # vm
        vm = STLScVmRaw( [ STLVmFlowVar(name="ip_src",
                                              min_value="16.0.0.0",
                                              max_value="16.0.0.254",
                                              size=4, op="inc"),


                           STLVmWrFlowVar(fv_name="ip_src", pkt_offset= "IP.src" ),

                           STLVmFixIpv4(offset = "IP"), # fix checksum
                          ]
                         ,split_by_field = "ip_src"                                 1
                       )
1 Deprecated split by field. not used any more (post v2.08)
Some rules regarding split stream variables and burst/multi-burst
  • When using burst/multi-burst, the number of packets are split to the defualt number of threads specified in the YAML cofiguraiton file, without any need to explicitly split the threads.

  • When the number of packets in a burst is smaller than the number of threads, one thread handles the burst.

  • In the case of a stream with a burst of 1 packet, only the first DP thread handles the stream.

2.10.15. Tutorial: Field Engine, Null stream

The following example creates a stream with no packets. The example uses the inter-stream gap (ISG) of the Null stream, and then starts a new stream. Essentially, this uses one property of the stream (ISG) without actually including packets in the stream.

This method can create loops like the following:

Figure 16. Null stream
  1. S1 - Sends a burst of packets, then proceed to stream NULL.

  2. NULL - Waits the inter-stream gap (ISG) time, then proceed to S1.

Null stream configuration:

  1. Mode: Burst

  2. Number of packets: 0

2.10.16. Tutorial: Field Engine, Stream Barrier (Split)

(Future Feature - not yet implemented)

In some situations, it is necessary to split streams into threads in such a way that specific streams will continue only after all the threads have passed the same path. In the figure below, a barrier ensures that stream S3 starts only after all threads of S2 are complete.

Figure 17. Stream Barrier

2.10.17. Tutorial: PCAP file to one stream

Goal

Load a stream template packet from a pcap file instead of Scapy.

Assumption: The pcap file contains only one packet. If the pcap file contains more than one packet, this procedure loads only the first packet.


    def get_streams (self, direction = 0, **kwargs):
        return [STLStream(packet =
                          STLPktBuilder(pkt ="stl/yaml/udp_64B_no_crc.pcap"), # path relative to pwd   1
                           mode = STLTXCont(pps=10)) ]
1 Takes the packet from the pcap file, relative to the directory in which you are running the script.

    def get_streams (self, direction = 0, **kwargs):
        return [STLStream(packet = STLPktBuilder(pkt ="yaml/udp_64B_no_crc.pcap",
                                                 path_relative_to_profile = True), 1
                         mode = STLTXCont(pps=10)) ]
1 Takes the packet from the pcap file, relative to the directory of the profile file location.

2.10.18. Tutorial: Teredo tunnel (IPv6 over IPv4)

The following example demonstrates creating an IPv6 packet within an IPv4 packet, and creating a range of IP addresses.

    def create_stream (self):
        # Teredo Ipv6 over Ipv4
        pkt =  Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/
              UDP(dport=3797,sport=3544)/
              IPv6(dst="2001:0:4137:9350:8000:f12a:b9c8:2815",
                   src="2001:4860:0:2001::68")/
              UDP(dport=12,sport=1025)/ICMPv6Unknown()

        vm = STLScVmRaw( [
                            # tuple gen for inner Ipv6
                            STLVmTupleGen ( ip_min="16.0.0.1", ip_max="16.0.0.2",
                                            port_min=1025, port_max=65535,
                                            name="tuple"),                      1

                             STLVmWrFlowVar (fv_name="tuple.ip",
                                             pkt_offset= "IPv6.src",
                                             offset_fixup=12 ),                 2
                             STLVmWrFlowVar (fv_name="tuple.port",
                                             pkt_offset= "UDP:1.sport" )        3
                          ]
                       )
1 Defines a stream struct called tuple with the following variables: tuple.ip, tuple.port
2 Writes a stream tuple.ip variable with an offset determined by the IPv6.src offset plus the offset_fixup of 12 bytes (only 4 LSB).
3 Writes a stream tuple.port variable into the second UDP header.

2.10.19. Tutorial: Mask instruction

STLVmWrMaskFlowVar is single-instruction-multiple-data Field Engine instruction. The pseudocode is as follows:

Pseudocode
        uint32_t val=(cast_to_size)rd_from_variable("name") # read flow-var
        val+=m_add_value                                    # add value

        if (m_shift>0) {                                    # shift
            val=val<<m_shift
        }else{
            if (m_shift<0) {
                val=val>>(-m_shift)
            }
        }

        pkt_val=rd_from_pkt(pkt_offset)                     # RMW
        pkt_val = (pkt_val & ~m_mask) | (val & m_mask)
        wr_to_pkt(pkt_offset,pkt_val)
Example 1

In this example, STLVmWrMaskFlowVar casts a stream variable with 2 bytes to be 1 byte.

        vm = STLScVmRaw( [ STLVmFlowVar(name="mac_src",
                                        min_value=1,
                                        max_value=30,
                                        size=2, op="dec",step=1),
                           STLVmWrMaskFlowVar(fv_name="mac_src",
                                              pkt_offset= 11,
                                              pkt_cast_size=1,
                                              mask=0xff) # mask command ->write it as one byte
                          ]
                       )
Example 2

In this example, STLVmWrMaskFlowVar shifts a variable by 8, which effectively multiplies by 256.


        vm = STLScVmRaw( [ STLVmFlowVar(name="mac_src",
                                        min_value=1,
                                        max_value=30,
                                        size=2, op="dec",step=1),
                           STLVmWrMaskFlowVar(fv_name="mac_src",
                                              pkt_offset= 10,
                                              pkt_cast_size=2,
                                              mask=0xff00,
                                              shift=8) # take the var shift it 8 (x256) write only to LSB
                          ]
                       )
Table 10. Output
value

0x0100

0x0200

0x0300

Example 3

In this example, STLVmWrMaskFlowVar instruction to generate the values shown in the table below as offset values for pkt_offset.

        vm = STLScVmRaw( [ STLVmFlowVar(name="mac_src",
                                        min_value=1,
                                        max_value=30,
                                        size=2,
                                        op="dec",step=1),
                           STLVmWrMaskFlowVar(fv_name="mac_src",
                                              pkt_offset= 10,
                                              pkt_cast_size=1,
                                              mask=0x1,
                                              shift=-1)         1
                          ]
                       )
1 Divides the value of mac_src by 2, and writes the LSB. For every two packets, the value written is changed.
Table 11. Output
value

0x00

0x00

0x01

0x01

0x00

0x00

0x01

0x01

2.10.20. Tutorial: Advanced traffic profile

Goal
  • Define a different profile to operate in each traffic direction.

  • Define a different profile for each port.

  • Tune a profile tune by the arguments of tunables.

Every traffic profile must define the following function:

def get_streams (self, direction = 0, **kwargs)

direction is a mandatory field, required for any profile being loaded.

A profile can be given any key-value pairs which can be used to customize this profile. These are called "tunables".

The profile defines which tunables can be input to customize output.

Usage notes for defining parameters
  • All parameters require default values.

  • A profile must be loadable with no parameters specified.

  • **kwargs (see Python documentation for information about keyworded arguments) contain all of the automatically provided values which are not tunables.

  • Every tuanble must be expressed as key-value pair with default value.

For example, for the profile below, pcap_with_vm.py:

  • The profile receives direction as a tunable and mandatory field.

  • The profile defines 4 additional tunables.

  • Automatic values such as port_id which are not tunables will be provided on kwargs.

def get_streams (self,
                 direction = 0,
                 ipg_usec = 10.0,
                 loop_count = 5,
                 ip_src_range = None,
                 ip_dst_range = {'start' : '10.0.0.1', 'end': '10.0.0.254'},
                 **kwargs)
Direction

direction is a tunable that is always provided by the API/console when loading a profile, but it can be overridden by the user. It is used to make the traffic profile more usable - for example, as a bi-directional profile. However, the profile can ignore this parameter.

By default, ‘direction` is equal to port_id % 2, so even numbered ports are provided with '0’ and the odd numbered ports with '1'.

def get_streams (self, direction = 0,**kwargs):
    if direction = 0:
        rate =100                                       1
    else:
        rate =200
    return [STLHltStream(tcp_src_port_mode = 'decrement',
                         tcp_src_port_count = 10,
                         tcp_src_port = 1234,
                         tcp_dst_port_mode = 'increment',
                         tcp_dst_port_count = 10,
                         tcp_dst_port = 1234,
                         name = 'test_tcp_ranges',
                         direction = direction,
                         rate_pps = rate,
                         ),
           ]
1 Specifies different rates (100 and 200) based on direction.
$start -f ex1.py -a

For 4 interfaces:

  • Interfaces 0 and 2: direction 0

  • Interfaces 1 and 3: direction 1

The rate changes accordingly.

Customzing Profiles Using 'port_id'

Keyworded arguments (**kwargs) provide default values that are passed along to the profile.

In the following, port_id (port ID for the profile) is a **kwarg. Using port_id, you can define a complex profile based on different ID of ports, providing a different profile for each port.


def create_streams (self, direction = 0, **args):

    port_id = args.get('port_id')

    if port_id == 0:
     return [STLHltStream(tcp_src_port_mode = 'decrement',
                         tcp_src_port_count = 10,
                         tcp_src_port = 1234,
                         tcp_dst_port_mode = 'increment',
                         tcp_dst_port_count = 10,
                         tcp_dst_port = 1234,
                         name = 'test_tcp_ranges',
                         direction = direction,
                         rate_pps = rate,
                         ),
           ]

   if port_id == 1:
        return STLHltStream(
                #enable_auto_detect_instrumentation = '1', # not supported yet
                ip_dst_addr = '192.168.1.3',
                ip_dst_count = '1',
                ip_dst_mode = 'increment',
                ip_dst_step = '0.0.0.1',
                ip_src_addr = '192.168.0.3',
                ip_src_count = '1',
                ip_src_mode = 'increment',
                ip_src_step = '0.0.0.1',
                l3_imix1_ratio = 7,
                l3_imix1_size = 70,
                l3_imix2_ratio = 4,
                l3_imix2_size = 570,
                l3_imix3_ratio = 1,
                l3_imix3_size = 1518,
                l3_protocol = 'ipv4',
                length_mode = 'imix',
                #mac_dst_mode = 'discovery', # not supported yet
                mac_src = '00.00.c0.a8.00.03',
                mac_src2 = '00.00.c0.a8.01.03',
                pkts_per_burst = '200000',
                rate_percent = '0.4',
                transmit_mode = 'continuous',
                vlan_id = '1',
                direction = direction,
                )

   if port_id = 3:
         ..
Full example using the TRex Console

The following command displays information about tunables for the pcap_with_vm.py traffic profile.

-=TRex Console v1.1=-

Type 'help' or '?' for supported actions

trex>profile -f stl/pcap_with_vm.py

Profile Information:


General Information:
Filename:         stl/pcap_with_vm.py
Stream count:          5

Specific Information:
Type:             Python Module
Tunables:         ['direction = 0', 'ip_src_range = None', 'loop_count = 5', 'ipg_usec = 10.0',
                   "ip_dst_range = {'start': '10.0.0.1', 'end': '10.0.0.254'}"]

trex>

One can provide tunables on all those fields. The following command changes some:

trex>start -f stl/pcap_with_vm.py -t ipg_usec=15.0,loop_count=25

Removing all streams from port(s) [0, 1, 2, 3]:              [SUCCESS]


Attaching 5 streams to port(s) [0]:                          [SUCCESS]


Attaching 5 streams to port(s) [1]:                          [SUCCESS]


Attaching 5 streams to port(s) [2]:                          [SUCCESS]


Attaching 5 streams to port(s) [3]:                          [SUCCESS]


Starting traffic on port(s) [0, 1, 2, 3]:                    [SUCCESS]

61.10 [ms]

trex>

The following command customizes these to different ports:


trex>start -f stl/pcap_with_vm.py --port 0 1 -t ipg_usec=15.0,loop_count=25#ipg_usec=100,loop_count=300

Removing all streams from port(s) [0, 1]:                    [SUCCESS]


Attaching 5 streams to port(s) [0]:                          [SUCCESS]


Attaching 5 streams to port(s) [1]:                          [SUCCESS]


Starting traffic on port(s) [0, 1]:                          [SUCCESS]

51.00 [ms]

trex>

2.10.21. Tutorial: Per stream statistics

  • Per stream statistics are implemented using hardware assist when possible (examples: Intel X710/XL710 NIC flow director rules).

  • With other NICs (examples: Intel I350, 82599), per stream statistics are implemented in software.

  • Implementation:

    • User chooses 32-bit packet group ID (pg_id) for each stream that need statistic reporting. Same pg_id can be used for more than one stream. In this case, statistics for all streams with the same pg_id will be combined.

    • The IPv4 identification (or IPv6 flow label in case of IPv6 packet) field of the stream is changed to a value within the reserved range 0xff00 to 0xffff (0xff00 to 0xfffff in case of IPv6). Note that if a stream for which no statistics are needed has an IPv4 Id (or IPv6 flow label) in the reserved range, it is changed (the left bit becomes 0).

    • Software implementation: Hardware rules are used to direct packets from relevant streams to rx threads, where they are counted.

    • Hardware implementation: Hardware rules are inserted to count packets from relevant streams.

  • Summed up statistics (per stream, per port) is sent using a ZMQ channel to clients upon request.

    Limitations
  • The feature supports only following packet types.

    • IPv4 over Ethernet.

    • IPv4 with one VLAN tag (except 82599 which does not support this type of packet).

    • IPv6 over Ethernet (except 82599 which does not support this type of packet).

    • IPv6 with one VLAN tag (except 82599 which does not support this type of packet).

    • Since version 2.21, also QinQ (two vlan tags) is supported if using “- - software” command line argument. Details here.

  • Maximum number of concurrent streams (with different pg_id) on which statistics may be collected is according to following table:

Table 12. Maximum concurrent streams for flow statistics collecting
NIC type Max streams supported using HW filters Using “- - software” (since version 2.23)

i350

255

1023

x710

127

1023

xl710

255

1023

82599

127

1023

Mellanox

127

127

virtio/vmxnet/other virtual NICs (Always working implicitly in “- - software” mode

1023

1023

  • On x710/xl710 cards, all rx bytes counters (rx-bps, rx-bps-L1, …) are not supported. This is because we use hardware counters which support only packets count on these cards.
    Starting from version 2.21, you can specify “--no-hw-flow-stat” command line argument in order to make x710 behave like other cards, and count statistics in software. This will enable RX byte count support, but will limit the total rate of streams we can count.

Two examples follow, one using the console and the other using the Python API.

Console

The following simple traffic profile defines 2 streams and configures them with 2 different PG IDs.

File

stl/flow_stats.py


class STLS1(object):

    def get_streams (self, direction = 0):
        return [STLStream(packet = STLPktBuilder(pkt ="stl/yaml/udp_64B_no_crc.pcap"),
                          mode = STLTXCont(pps = 1000),
                          flow_stats = STLFlowStats(pg_id = 7)), 1

                STLStream(packet = STLPktBuilder(pkt ="stl/yaml/udp_594B_no_crc.pcap"),
                          mode = STLTXCont(pps = 5000),
                          flow_stats = STLFlowStats(pg_id = 12)) 2
               ]

1 Assigned to PG ID 7
2 Assigned to PG ID 12

The following command injects this to the console and uses the textual user interface (TUI) to display the TRex activity:

trex>start -f stl/flow_stats.py --port 0

Removing all streams from port(s) [0]:                       [SUCCESS]


Attaching 2 streams to port(s) [0]:                          [SUCCESS]


Starting traffic on port(s) [0]:                             [SUCCESS]

155.81 [ms]

trex>tui

Streams Statistics

   PG ID    |        12         |         7
 --------------------------------------------------
 Tx pps     |         5.00 Kpps |        999.29 pps   #1
 Tx bps L2  |        23.60 Mbps |       479.66 Kbps
 Tx bps L1  |        24.40 Mbps |       639.55 Kbps
 ---        |                   |
 Rx pps     |         5.00 Kpps |        999.29 pps   #2
 Rx bps     |               N/A |               N/A   #3
 ----       |                   |
 opackets   |            222496 |             44500   #4
 ipackets   |            222496 |             44500
 obytes     |         131272640 |           2670000
 ibytes     |               N/A |               N/A   #3
 -----      |                   |
 opackets   |      222.50 Kpkts |       44.50 Kpkts
 ipackets   |      222.50 Kpkts |       44.50 Kpkts
 obytes     |         131.27 MB |           2.67 MB
 ibytes     |               N/A |               N/A   #3
1 Tx bandwidth of the streams matches the configured values.
2 Rx bandwidth (999.29 pps) matches the Tx bandwidth (999.29 pps), indicating that there were no drops.
3 RX byte count is not supported on this platform (no hardware support for byte count), so TRex displays N/A. You can add “--no-hw-flow-stat” command line argument, in order to count everything in software, but max rate of streams that can be tracked will be lower.
4 opackets/ipackets/obytes/ibytes appear twice. First time with accurate number, and second time formatted.
Flow Stats Using The Python API

The Python API example uses the following traffic profile:

def rx_example (tx_port, rx_port, burst_size):

    # create client
    c = STLClient()

    try:
        pkt = STLPktBuilder(pkt = Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/
                                  UDP(dport=12,sport=1025)/IP()/'a_payload_example')

        s1 = STLStream(name = 'rx',
                       packet = pkt,
                       flow_stats = STLFlowStats(pg_id = 5),    1
                       mode = STLTXSingleBurst(total_pkts = 5000,
                                               percentage = 80
                                               ))

        # connect to server
        c.connect()

        # prepare our ports - TX/RX
        c.reset(ports = [tx_port, rx_port])

        # add the stream to the TX port
        c.add_streams([s1], ports = [tx_port])

        # start and wait for completion
        c.start(ports = [tx_port])
        c.wait_on_traffic(ports = [tx_port])

        # fetch stats for PG ID 5
        flow_stats = c.get_stats()['flow_stats'].get(5)    2

        tx_pkts  = flow_stats['tx_pkts'].get(tx_port, 0)   2
        tx_bytes = flow_stats['tx_bytes'].get(tx_port, 0)  2
        rx_pkts  = flow_stats['rx_pkts'].get(rx_port, 0)   2
1 Configures the stream to use PG ID 5.
2 The structure of the object 'flow_stats' is described below.

2.10.22. Tutorial: flow_stats object structure

The flow_stats object is a dictionary whose keys are the configured PG IDs. The next level is a dictionary containing tx_pkts, tx_bytes, rx_pkts, and rx_bytes (on supported HW). Each of these keys contains a dictionary of per port values.

The following shows a flow_stats object for 3 PG IDs after a specific run:

{
 5: {'rx_pkts'  : {0: 0, 1: 0, 2: 500000, 3: 0, 'total': 500000},
     'tx_bytes' : {0: 0, 1: 39500000, 2: 0, 3: 0, 'total': 39500000},
     'tx_pkts'  : {0: 0, 1: 500000, 2: 0, 3: 0, 'total': 500000}},

 7: {'rx_pkts'  : {0: 0, 1: 0, 2: 0, 3: 288, 'total': 288},
     'tx_bytes' : {0: 17280, 1: 0, 2: 0, 3: 0, 'total': 17280},
     'tx_pkts'  : {0: 288, 1: 0, 2: 0, 3: 0, 'total': 288}},

 12: {'rx_pkts' : {0: 0, 1: 0, 2: 0, 3: 1439, 'total': 1439},
      'tx_bytes': {0: 849600, 1: 0, 2: 0, 3: 0, 'total': 849600},
      'tx_pkts' : {0: 1440, 1: 0, 2: 0, 3: 0, 'total': 1440}}
}

2.10.23. Tutorial: Per stream latency/jitter/packet errors

  • Per stream latency/jitter is implemented by software. This is an extension of the per stream statistics. Meaning, whenever you choose to get latency info for a stream, the statistics described in the "Per stream statistics" section is also available.

  • Implementation:

    • User chooses 32-bit packet group ID (pg_id) for each stream that need latency reporting. pg_id should be unique per stream.

    • The IPv4 identification field (or IPv6 flow label in case of IPv6 packet) of the stream is changed to some defined constant value (in the reserved range described in the "per stream statistics" section), in order to signal the hardware to pass the stream to software.

    • Last 16 bytes of the packet payload is used to pass needed information. Information contains ID of the stream, packet sequence number (per stream), timestamp of packet transmission.

  • Gathered info (per stream) is sent using ZMQ channel to clients upon request.

    Limitations
  • The feature supports only following packet types (Unless using “--software” command line arg. See details here. Using this, all packet types are supported):

    • IPv4 over Ethernet

    • IPv4 with one VLAN tag (except 82599 which does not support this type of packet)

    • IPv6 over Ethernet (except 82599 which does not support this type of packet)

    • IPv6 with one VLAN tag (except 82599 which does not support this type of packet)

  • Packets must contain at least 16 bytes of payload.

  • Each stream must have unique pg_id number. This also means that a given "latency collecting" stream can’t be transmitted from two interfaces in parallel (internally it means that there are two streams).

  • Maximum number of concurrent streams (with different pg_id) on which latency info may be collected: 128 (This is in addition to the streams which collect per stream statistics).

  • Global multiplier does not apply to this type of stream. The reason is that latency streams are processed by software, so multiplying them might accidently overwhelm the RX core. This means that if you have profile with 1 latency stream, and 1 non latency stream, and you change the traffic multipler, latency stream keeps the same rate. If you want to change the rate of a latency stream, you need to manually edit your profile file. Usually this is not necessary, since normally you stress the system using non latency stream, and (in parallel) measure latency using constant rate latency stream.

Important

Latency streams are not supported in full line rate like normal streams. Both from transmit and receive point of view. This is a design consideration to keep the latency measurement accurate while preserving CPU resources. One of the reasons for doing so is that in most cases it is enough to have a latency stream in low rate. For example, if the required latency resolution is 10usec, there is no need to send latency stream in a speed higher than 100KPPS. Usually queues are built over time, so it is not possible that one packet will have latency and another packet in the same path will not have the same latency. The none latency streams could be in full line rate, to load the DUT, while the low speed latency streams will measure the latency of this path. Don’t make the total rate of latency streams higher than 5MPPS.

Two examples follow. One using the console and the other using the Python API.

Console

The following simple traffic profile defines 2 streams and configures them with 2 different PG IDs.

File

stl/flow_stats_latency.py


class STLS1(object):

    def get_streams (self, direction = 0):
        return [STLStream(packet = STLPktBuilder(pkt ="stl/yaml/udp_64B_no_crc.pcap"),
                          mode = STLTXCont(pps = 1000),
                          flow_stats = STLFlowLatencyStats(pg_id = 7)), 1

                STLStream(packet = STLPktBuilder(pkt ="stl/yaml/udp_594B_no_crc.pcap"),
                          mode = STLTXCont(pps = 5000),
                          flow_stats = STLFlowLatencyStats(pg_id = 12)) 2
               ]

1 Assigned to PG ID 7 , PPS would be 1000 regardless of the multplier
2 Assigned to PG ID 12, PPS would be 5000 regardless of the multplier

The following command injects this to the console and uses the textual user interface (TUI) to display the TRex activity:

trex>start -f stl/flow_stats.py --port 0

trex>tui

Latency Statistics (usec)

   PG ID     |       7       |       12
 ----------------------------------------------
 Max latency  |              0 |              0 #1
 Avg latency  |              5 |              5 #2
 -- Window -- |                |
 Last (max)   |              3 |              4 #3
 Last-1       |              3 |              3
 Last-2       |              4 |              4
 Last-3       |              4 |              3
 Last-4       |              4 |              4
 Last-5       |              3 |              4
 Last-6       |              4 |              3
 Last-7       |              4 |              3
 Last-8       |              4 |              4
 Last-9       |              4 |              3
 ---          |                |
 Jitter       |              0 |              0 #4
 ----         |                |
 Errors       |              0 |              0 #5
1 Maximum latency measured over the stream lifetime (in usec).
2 Average latency over the stream lifetime (usec).
3 Maximum latency measured between last two data reads from server (We currently read every 0.5 second). Numbers below are maximum latency for previous measuring periods, so we get latency history for last few seconds.
4 Jitter of latency measurements.
5 Indication of number of errors (it is the sum of seq_too_high and seq_too_low. You can see description in Python API doc below). In the future it will be possible to zoom in, to see specific counters. For now, if you need to see specific counters, you can use the Python API.

An example of API usage is as follows


    stats = c.get_stats()

    flow_stats = stats['flow_stats'].get(5)
    lat_stats = stats['latency'].get(5)                 1


    tx_pkts  = flow_stats['tx_pkts'].get(tx_port, 0)
    tx_bytes = flow_stats['tx_bytes'].get(tx_port, 0)
    rx_pkts  = flow_stats['rx_pkts'].get(rx_port, 0)
    drops = lat_stats['err_cntrs']['dropped']
    ooo = lat_stats['err_cntrs']['out_of_order']
    dup = lat_stats['err_cntrs']['dup']
    sth = lat_stats['err_cntrs']['seq_too_high']
    stl = lat_stats['err_cntrs']['seq_too_low']
    lat = lat_stats['latency']
    jitter = lat['jitter']
    avg = lat['average']
    tot_max = lat['total_max']
    last_max = lat['last_max']
    hist = lat ['histogram']

    # lat_stats will be in this format

    latency_stats ==  {
         'err_cntrs':{                  # error counters 2
            u'dup':0,                   # Same sequence number was received twice in a row
            u'out_of_order':0,          # Packets received with sequence number too low (We assume it is reorder)
            u'dropped':0                # Estimate of number of packets that were dropped (using seq number)
            u'seq_too_high':0,          # seq number too high events
            u'seq_too_low':0,           # seq number too low events
         },
         'latency':{
            'jitter':0,                 # in usec
            'average':15.2,             # average latency (usec)
            'last_max':0,               # last 0.5 sec window maximum latency (usec)
            'total_max':44,             # maximum latency (usec)
            'histogram':[               # histogram of latency
               {
                  u'key':20,            # bucket counting packets with latency in the range 20 to 30 usec
                  u'val':489342         # number of samples that hit this bucket's range
               },
               {
                  u'key':30,
                  u'val':10512
               },
               {
                  u'key':40,
                  u'val':143
               },
               {
                  'key':0,              # bucket counting packets with latency in the range 0 to 10 usec
                  'val':3
               }
            ]
         }
      },

1 Get the Latency dictionary
2 For calculating packet error events, we add sequence number to each packet’s payload. We decide what went wrong only according to sequence number of last packet received and that of the previous packet. seq_too_low and seq_too_high count events we see. dup, out_of_order and dropped are heuristics we apply to try and understand what happened. They will be accurate in common error scenarios. We describe few scenarios below to help understand this.
Error counters scenarios

Scenario 1: Received packet with seq num 10, and another one with seq num 10. We increment dup and seq_too_low by 1.
Scenario 2: Received pacekt with seq num 10 and then packet with seq num 15. We assume 4 packets were dropped, and increment dropped by 4, and seq_too_high by 1. We expect next packet to arrive with sequence number 16.
Scenario 2 continue: Received packet with seq num 11. We increment seq_too_low by 1. We increment out_of_order by 1. We decrement dropped by 1. (We assume here that one of the packets we considered as dropped before, actually arrived out of order).

2.10.24. Tutorial: HLT traffic profile

The traffic_config API has set of arguments for specifying streams - in particular, the packet template, which field, and how to send it. It is possible to define a traffic profile using HTTAPI arguments. The API creates native Scapy/Field Engine instructions. For limitations see here.


class STLS1(object):
    '''
    Create 2 Eth/IP/UDP streams with different packet size:
    First stream will start from 64 bytes (default) and will increase until max_size (9,216)
    Seconds stream will decrease the packet size in reverse way
    '''

    def create_streams (self):
        max_size = 9*1024
        return [STLHltStream(length_mode = 'increment',
                             frame_size_max = max_size,
                             l3_protocol = 'ipv4',
                             ip_src_addr = '16.0.0.1',
                             ip_dst_addr = '48.0.0.1',
                             l4_protocol = 'udp',
                             udp_src_port = 1025,
                             udp_dst_port = 12,
                             rate_pps = 1,
                             ),
                STLHltStream(length_mode = 'decrement',
                             frame_size_max = max_size,
                             l3_protocol = 'ipv4',
                             ip_src_addr = '16.0.0.1',
                             ip_dst_addr = '48.0.0.1',
                             l4_protocol = 'udp',
                             udp_src_port = 1025,
                             udp_dst_port = 12,
                             rate_pps = 1,
                             )
               ]

    def get_streams (self, direction = 0, **kwargs):
        return self.create_streams()

The following command, within a bash window, runs the traffic profile with the simulator to generate pcap file.

[bash]>./stl-sim -f stl/hlt/hlt_udp_inc_dec_len_9k.py -o b.pcap -l 10

The following commands, within a bash window, convert to native JSON or YAML.

[bash]>./stl-sim -f stl/hlt/hlt_udp_inc_dec_len_9k.py --json
[bash]>./stl-sim -f stl/hlt/hlt_udp_inc_dec_len_9k.py --yaml

Alternatively, use the following command to convert to a native Python profile.

[bash]>./stl-sim -f stl/hlt/hlt_udp_inc_dec_len_9k.py --native
Auto-generated code
# !!! Auto-generated code !!!
from trex_stl_lib.api import *

class STLS1(object):
    def get_streams(self):
        streams = []

        packet = (Ether(src='00:00:01:00:00:01', dst='00:00:00:00:00:00', type=2048) /
                  IP(proto=17, chksum=5882, len=9202, ihl=5L, id=0) /
                  UDP(dport=12, sport=1025, len=9182, chksum=55174) /
                  Raw(load='!' * 9174))
        vm = STLScVmRaw([CTRexVmDescFlowVar(name='pkt_len', size=2, op='inc',
                          init_value=64, min_value=64, max_value=9216, step=1),
                         CTRexVmDescTrimPktSize(fv_name='pkt_len'),
                         CTRexVmDescWrFlowVar(fv_name='pkt_len',
                         pkt_offset=16, add_val=-14, is_big=True),
                         CTRexVmDescWrFlowVar(fv_name='pkt_len',
                         pkt_offset=38, add_val=-34, is_big=True),
                         CTRexVmDescFixIpv4(offset=14)], split_by_field = 'pkt_len')
        stream = STLStream(packet = CScapyTRexPktBuilder(pkt = packet, vm = vm),
                           mode = STLTXCont(pps = 1.0))
        streams.append(stream)

        packet = (Ether(src='00:00:01:00:00:01', dst='00:00:00:00:00:00', type=2048) /
                  IP(proto=17, chksum=5882, len=9202, ihl=5L, id=0) /
                  UDP(dport=12, sport=1025, len=9182, chksum=55174) /
                  Raw(load='!' * 9174))
        vm = STLScVmRaw([CTRexVmDescFlowVar(name='pkt_len', size=2, op='dec',
                         init_value=9216, min_value=64,
                         max_value=9216, step=1),
                         CTRexVmDescTrimPktSize(fv_name='pkt_len'),
                         CTRexVmDescWrFlowVar(fv_name='pkt_len', pkt_offset=16,
                         add_val=-14, is_big=True),
                         CTRexVmDescWrFlowVar(fv_name='pkt_len',
                         pkt_offset=38, add_val=-34, is_big=True),
                         CTRexVmDescFixIpv4(offset=14)], split_by_field = 'pkt_len')
        stream = STLStream(packet = CScapyTRexPktBuilder(pkt = packet, vm = vm),
                           mode = STLTXCont(pps = 1.0))
        streams.append(stream)

        return streams

def register():
    return STLS1()

Use the following command within the TRex console to run the profile.

TRex>start -f stl/hlt/hlt_udp_inc_dec_len_9k.py -m 10mbps -a

2.11. Functional Tutorials

On functional tests we demonstrate a way to test certain cases which does not require high bandwidth but instead require more flexibility such as fetching all the packets on the RX side.

2.11.1. Tutorial: Testing Dot1Q VLAN tagging

Goal

Generate a Dot1Q packet with a vlan tag and verify the returned packet is on the same vlan

File

stl_functional.py

The below example has been reduced to be concise, please refer to the file above for the full working example

#passed a connected client object and two ports
def test_dot1q (c, rx_port, tx_port):

    # activate service mode on RX code
    c.set_service_mode(ports = rx_port)

    # generate a simple Dot1Q
    pkt = Ether() / Dot1Q(vlan = 100) / IP()

    # start a capture
    capture = c.start_capture(rx_ports = rx_port)

    # push the Dot1Q packet to TX port... we need 'force' because this is under service mode
    print('\nSending 1 Dot1Q packet(s) on port {}'.format(tx_port))

    c.push_packets(ports = tx_port, pkts = pkt, force = True)
    c.wait_on_traffic(ports = tx_port)

    rx_pkts = []
    c.stop_capture(capture_id = capture['id'], output = rx_pkts)

    print('\nRecived {} packets on port {}:\n'.format(len(rx_pkts), rx_port))

    c.set_service_mode(ports = rx_port, enabled = False)

    # got back one packet
    assert(len(rx_pkts) == 1)
    rx_scapy_pkt = Ether(rx_pkts[0]['binary'])

    # it's a Dot1Q with the same VLAN
    assert('Dot1Q' in rx_scapy_pkt)
    assert(rx_scapy_pkt.vlan == 100)


    rx_scapy_pkt.show2()

2.11.2. Tutorial: Testing IPv4 ping - echo request / echo reply

Goal

Generate a ICMP echo request from one interface to another one and validate the response

File

stl_functional.py

# test a echo request / echo reply
def test_ping (c, tx_port, rx_port):

    # activate service mode on RX code
    c.set_service_mode(ports = [tx_port, rx_port])

    # fetch the config
    tx_port_attr = c.get_port_attr(port = tx_port)
    rx_port_attr = c.get_port_attr(port = rx_port)

    assert(tx_port_attr['layer_mode'] == 'IPv4')
    assert(rx_port_attr['layer_mode'] == 'IPv4')

    pkt = Ether() / IP(src = tx_port_attr['src_ipv4'], dst = rx_port_attr['src_ipv4']) / ICMP(type = 8)

    # start a capture on the sending port
    capture = c.start_capture(rx_ports = tx_port)

    print('\nSending ping request on port {}'.format(tx_port))

    # send the ping packet
    c.push_packets(ports = tx_port, pkts = pkt, force = True)
    c.wait_on_traffic(ports = tx_port)

    # fetch the packet
    rx_pkts = []
    c.stop_capture(capture_id = capture['id'], output = rx_pkts)

    print('\nRecived {} packets on port {}:\n'.format(len(rx_pkts), tx_port))

    c.set_service_mode(ports = rx_port, enabled = False)

    # got back one packet
    assert(len(rx_pkts) == 1)
    rx_scapy_pkt = Ether(rx_pkts[0]['binary'])

    # check for ICMP reply
    assert('ICMP' in rx_scapy_pkt)
    assert(rx_scapy_pkt['ICMP'].type == 0)

    rx_scapy_pkt.show2()

2.12. Services

Important The following section relies on service mode - please refer to service mode section for more details

2.12.1. Overview

While under service mode, TRex provides the ability to run services.

A service is an instance of a service type that has a certain request / response state machine.

Figure 18. Services Instances

For example, the ARP service type provides a way to create ARP request instances that can be then executed by TRex in a parallel way supporting up to ~1000 requests in parallel.

The following diagram illustrates how services fit in the general flow:

Figure 19. Services Execution Flow
Note A simple example

The simplest example of a service execution:

# import the ARP service
from trex_stl_lib.services.trex_stl_service_arp import STLServiceARP

# create a service context on port 0
ctx  = client.create_service_ctx(port = 0)

# generate single ARP request from 1.1.1.5 to 1.1.1.1
arp = STLServiceARP(ctx, src_ip = '1.1.1.5', dst_ip = '1.1.1.1')

# move to service mode and execute service
client.set_service_mode(ports = 0)
try:
    ctx.run(arp)
finally:
    client.set_service_mode(ports = 0, enabled = False)

# show the ARP result
print(arp.get_record())
Recieved ARP reply from: 1.1.1.1, hw: b8:46:dd:63:21:e4"

There are two main usages for services:

  • Customizing Tests

  • Control Plane Stress

2.12.2. Customizing Tests

Services provides an easy way to customize tests:

executing services can be used to dynamically acquire data prior to the test and then generate a test based on the results.

Note An example of using DHCP service to cutomize a test

Let’s assume that our topology includes a DHCP server which will allow traffic from previously leased addreses only. Without services we will not be able to statically generate a test that will be accepted by the server.

However, with services we can generate clients using the DHCP service type and used the leased addresses to generate traffic.

Figure 20. Services Two Phase Based Test

Let’s take a deep dive into how to use Python API to implement the above example:


# first we import the relevant service
from trex_stl_lib.services.trex_stl_service_dhcp import STLServiceDHCP

# next we generate a service context on the required port
# all services will be executed on the same port - there is no cross-port service execution
ctx  = client.create_service_ctx(port = 0)

# generate 100 clients from random MACs (random MAC function omitted)
# you can, of course, supply specific MAC addresses
dhcps = [STLServiceDHCP(mac = random_mac()) for _ in range(100)]

# now we execute the service context under service mode

client.set_service_mode(ports = 0)
try:
    ctx.run(dhcps)
finally:
    client.set_service_mode(ports = 0, enabled = False)


# inspect the DHCP execution result
for dhcp in dhcps:
    record = dhcp.get_record()
    print('client: MAC {0} - DHCP: {1}'.format(dhcp.get_mac(),record))

# let's filter all the DHCPs that successfuly moved to 'BOUND' state
# refer to the DHCP code reference to see all the available states
bounded_dhcps = [dhcp for dhcp in dhcps if dhcp.state == 'BOUND']

# we can use the above results to generate traffic from the leased addresses

streams = []
for bound_dhcp in bounded_dhcps:
    record = bound_dhcp.get_record()

    pkt = STLPktBuilder(pkt = Ether(src=record.client_mac)/
                              IP(src=record.client_ip,dst=record.server_ip)/
                              UDP)
    streams.append(STLStream(packet = pkt, mode = STLTXSingleBurst(total_pkts = 1000)))

# add streams and generate traffic
client.add_streams(ports = 0, streams = streams)
client.start(ports = 0, mult = '100%')
client.wait_on_traffic()

And here is how the output (partial) looks like:

client: MAC 3c:1d:08:91:7f:34 - DHCP: ip: 1.1.1.8,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC 21:3c:a3:3f:cb:a7 - DHCP: ip: 1.1.1.5,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC f9:ba:11:51:91:8b - DHCP: ip: 1.1.1.7,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC b8:46:dd:63:21:e4 - DHCP: ip: 1.1.1.11, server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC b8:38:f9:c7:1c:6e - DHCP: ip: 1.1.1.9,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC 44:27:f1:f3:9a:bd - DHCP: ip: 1.1.1.10, server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC cd:8d:c6:c9:5c:6a - DHCP: ip: 1.1.1.2,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC 51:ee:33:d9:d8:9f - DHCP: ip: 1.1.1.3,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC 75:f2:22:ce:86:47 - DHCP: ip: 1.1.1.4,  server_ip: 1.1.1.1, subnet: 255.255.255.0
client: MAC 19:bb:56:20:52:3b - DHCP: ip: 1.1.1.6,  server_ip: 1.1.1.1, subnet: 255.255.255.0

2.12.3. Control Plane Stress Tests

Another practical use-case of services is to simply use the first phase as the main phase and focus on generating many control plane requests.

For example, the same DHCP example can be used to stress out a DHCP server by generating many requests.

Now, even though service mode is slower that regular mode, and service context execution is even slower as we wait for response from the server there are still two major benefits:

  • Parallelism - When generating many service instances, there will be minimum impact on the total run time as we execute services in parallel

  • Flexibility - Putting aside performance, TRex services are written in Python and uses Scapy to generate traffic and thus are very easy to manipulate and custom fit

2.12.4. Currently Provided Services

Currently, the implemented services provided with TRex package are:

  • ARP - provides an ARP resolution for an IPv4 address

  • ICMPv4 - provides Ping IPv4 for an IPv4 address

  • DHCP - provides a DHCP bound/release lease address

We are planning to add more and hope for contribution in this area

2.12.5. A Detailed DHCP Example

Full DHCP example can be found under the following GitHub link:

2.12.6. Limitations

There is no limitation on the types of services that are being executed. It is possible to run ARP and DHCP in parallel if it is needed.

The only limitation is that services run under context which is bounded to a single port.

There is no way to forward response from another port to the context.

Also, the number of service instances per execution is currently limited to 1000.

2.13. PCAP Based Traffic Tutorials

2.13.1. PCAP Based Traffic

TRex provides a method of using a pre-recorded traffic as a profile template.

There are two main distinct ways of creating a profile or a test based on a PCAP.

  • Local PCAP push

  • Server based push

Local PCAP push

On this mode, the PCAP file is loaded locally by the Python client, transformed to a list of streams which each one contains a single packet and points to the next one.

This allows of a very flexible structure which can basically provide every functionality that a regular list of streams allow.

However, due to the overhead of processing and sending a list of streams this method is limited to a file size (on default 1MB)

Pros:

  • supports most CAP file formats

  • supports field engine

  • provides a way of locally manipulating packets as streams

  • supports same rate as regular streams

Cons:

  • limited in file size

  • high configuration time due to transmitting the CAP file as streams

Server based push

To provide also a way of injecting a much larger PCAP files, TRex also provides a server based push.

The mechansim is much different and it simply providing a server a PCAP file which in turn is loaded to the server and injected packet after packet.

This method provides an unlimited file size to be injected, and the overhead of setting up the server with the required configuration is much lower.

Pros:

  • no limitation of PCAP file size

  • no overhead in sending any size of PCAP to the server

Cons:

  • does not support field engine

  • support only PCAP and ERF formats

  • requires the file path to be accessible from the server

  • rate of transmition is usually limited by I/O performance and buffering (HDD)

2.13.2. Tutorial: Simple PCAP file - Profile

Goal

Load a pcap file with a number of packets, creating a stream with a burst value of 1 for each packet. The inter-stream gap (ISG) for each stream is equal to the inter-packet gap (IPG).

File

pcap.py

    def get_streams (self,
                     ipg_usec = 10.0,                           1
                     loop_count = 1):                           2

        profile = STLProfile.load_pcap(self.pcap_file,          3
                                       ipg_usec = ipg_usec,
                                       loop_count = loop_count)
1 The inter-stream gap in microseconds.
2 Loop count.
3 Input pcap file.
Figure 21. Example of multiple streams

The figure shows the streams for a pcap file with 3 packets, with a loop configured.

  • Each stream is configured to Burst mode with 1 packet.

  • Each stream triggers the next stream.

  • The last stream triggers the first with action_loop=loop_count if loop_count > 1.

The profile runs on one DP thread because it has a burst with 1 packet. (Split cannot work in this case).

To run this example, enter:

[bash]>./stl-sim -f stl/pcap.py --yaml

The following output appears:

$./stl-sim -f stl/pcap.py --yaml
- name: 1
  next: 2                      1
  stream:
    action_count: 0
    enabled: true
    flags: 0
    isg: 10.0
    mode:
      percentage: 100
      total_pkts: 1
      type: single_burst
    packet:
      meta: ''
    rx_stats:
      enabled: false
    self_start: true
    vm:
      instructions: []
      split_by_var: ''
- name: 2
  next: 3
  stream:
    action_count: 0
    enabled: true
    flags: 0
    isg: 10.0
    mode:
      percentage: 100
      total_pkts: 1
      type: single_burst
    packet:
      meta: ''
    rx_stats:
      enabled: false
    self_start: false
    vm:
      instructions: []
      split_by_var: ''
- name: 3
  next: 4
  stream:
    action_count: 0
    enabled: true
    flags: 0
    isg: 10.0
    mode:
      percentage: 100
      total_pkts: 1
      type: single_burst
    packet:
      meta: ''
    rx_stats:
      enabled: false
    self_start: false
    vm:
      instructions: []
      split_by_var: ''
- name: 4
  next: 5
  stream:
    action_count: 0
    enabled: true
    flags: 0
    isg: 10.0
    mode:
      percentage: 100
      total_pkts: 1
      type: single_burst
    packet:
      meta: ''
    rx_stats:
      enabled: false
    self_start: false
    vm:
      instructions: []
      split_by_var: ''
- name: 5
  next: 1                   2
  stream:
    action_count: 1         3
    enabled: true
    flags: 0
    isg: 10.0
    mode:
      percentage: 100
      total_pkts: 1
      type: single_burst
    packet:
      meta: ''
    rx_stats:
      enabled: false
    self_start: false       4
    vm:
      instructions: []
      split_by_var: ''
1 Each stream triggers the next stream.
2 The last stream triggers the first.
3 The current loop count is given in: action_count: 1
4 Self_start is enabled for the first stream, disabled for all other streams.

2.13.3. Tutorial: Simple PCAP file - API

For this case we can use the local push:

c = STLClient(server = "localhost")

try:

    c.connect()
    c.reset(ports = [0])

    d = c.push_pcap(pcap_file = "my_file.pcap",             # our local PCAP file
                    ports = 0,                              # use port 0
                    ipg_usec = 100,                         # IPG
                    count = 1)                              # inject only once

    c.wait_on_traffic()


    stats = c.get_stats()
    opackets = stats[port]['opackets']
    print("{0} packets were Tx on port {1}\n".format(opackets, port))

  except STLError as e:
      print(e)
      sys.exit(1)

  finally:
      c.disconnect()

2.13.4. Tutorial: PCAP file iterating over dest IP

For this case we can use the local push:

c = STLClient(server = "localhost")

try:

    c.connect()
    port = 0
    c.reset(ports = [port])

    vm = STLIPRange(dst = {'start': '10.0.0.1', 'end': '10.0.0.254', 'step' : 1})

    c.push_pcap(pcap_file = "my_file.pcap",             # our local PCAP file
                ports = port,                           # use 'port'
                ipg_usec = 100,                         # IPG
                count = 1,                              # inject only once
                vm = vm                                 # provide VM object
                )

    c.wait_on_traffic()

    stats = c.get_stats()
    opackets = stats[port]['opackets']
    print("{0} packets were Tx on port {1}\n".format(opackets, port))

  except STLError as e:
      print(e)
      sys.exit(1)

  finally:
      c.disconnect()

2.13.5. Tutorial: PCAP file with VLAN

This is a more intresting case where we can provide the push API a function hook. The hook will be called for each packet that is loaded from the PCAP file.

# generate a packet hook function with a VLAN ID
def packet_hook_generator (vlan_id):

    # this function will be called for each packet and will expect
    # the new packet as a return value
    def packet_hook (packet):
        packet = Ether(packet)

        if vlan_id >= 0 and vlan_id <= 4096:
            packet_l3 = packet.payload
            packet = Ether() / Dot1Q(vlan = vlan_id) / packet_l3

        return str(packet)

    return packet_hook

c = STLClient(server = "localhost")

try:

    c.connect()
    port = 0
    c.reset(ports = [port])

    vm = STLIPRange(dst = {'start': '10.0.0.1', 'end': '10.0.0.254', 'step' : 1})

    d = c.push_pcap(pcap_file = "my_file.pcap",
                    ports = port,
                    ipg_usec = 100,
                    count = 1,
                    packet_hook = packet_hook_generator(vlan_id = 1)
                    )

    c.wait_on_traffic()

    stats = c.get_stats()
    opackets = stats[port]['opackets']
    print("{0} packets were Tx on port {1}\n".format(opackets, port))

  except STLError as e:
      print(e)
      sys.exit(1)

  finally:
      c.disconnect()

2.13.6. Tutorial: PCAP file and Field Engine - Profile

The following example loads a pcap file to many streams, and attaches Field Engine program to each stream. For example, the Field Engine can change the IP.src of all the streams to a random IP address.


    def create_vm (self, ip_src_range, ip_dst_range):
        if not ip_src_range and not ip_dst_range:
            return None

        # until the feature of offsets will be fixed for PCAP use hard coded offsets

        vm = []

        if ip_src_range:
            vm += [STLVmFlowVar(name="src",
                                min_value = ip_src_range['start'],
                                max_value = ip_src_range['end'],
                                size = 4, op = "inc"),
                   #STLVmWrFlowVar(fv_name="src",pkt_offset= "IP.src")
                   STLVmWrFlowVar(fv_name="src",pkt_offset = 26)
                  ]

        if ip_dst_range:
            vm += [STLVmFlowVar(name="dst",
                                min_value = ip_dst_range['start'],
                                max_value = ip_dst_range['end'],
                                size = 4, op = "inc"),

                   #STLVmWrFlowVar(fv_name="dst",pkt_offset= "IP.dst")
                   STLVmWrFlowVar(fv_name="dst",pkt_offset = 30)
                   ]

        vm += [#STLVmFixIpv4(offset = "IP")
              STLVmFixIpv4(offset = 14)
              ]

        return vm


    def get_streams (self,
                     ipg_usec = 10.0,
                     loop_count = 5,
                     ip_src_range = None,
                     ip_dst_range = {'start' : '10.0.0.1',
                                        'end': '10.0.0.254'}):

        vm = self.create_vm(ip_src_range, ip_dst_range)                 1
        profile = STLProfile.load_pcap(self.pcap_file,
                                      ipg_usec = ipg_usec,
                                      loop_count = loop_count,
                                      vm = vm)                          2

        return profile.get_streams()
1 Creates Field Engine program.
2 Applies the Field Engine to all packets → converts to streams.
Table 13. Output
pkt IPv4 flow

1

10.0.0.1

1

2

10.0.0.1

1

3

10.0.0.1

1

4

10.0.0.1

1

5

10.0.0.1

1

6

10.0.0.1

1

7

10.0.0.2

2

8

10.0.0.2

2

9

10.0.0.2

2

10

10.0.0.2

2

11

10.0.0.2

2

12

10.0.0.2

2

2.13.7. Tutorial: Huge server side PCAP file

Now we would like to use the remote push API. This will require the file path to be visible to the server.

c = STLClient(server = "localhost")

try:

    c.connect()
    c.reset(ports = [0])

    # use an absolute path so the server can reach this
    pcap_file = os.path.abspath(pcap_file)

    c.push_remote(pcap_file = pcap_file,
                  ports = 0,
                  ipg_usec = 100,
                  count = 1)

    c.wait_on_traffic()


    stats = c.get_stats()
    opackets = stats[port]['opackets']
    print("{0} packets were Tx on port {1}\n".format(opackets, port))

  except STLError as e:
      print(e)
      sys.exit(1)

  finally:
      c.disconnect()

2.13.8. Tutorial: A long list of PCAP files of varied sizes

This is also a good candidate for the remote push API. The total overhead for sending the PCAP files will be high if the list is long, so we would prefer to inject them with remote API and to save the transmition of the packets.

c = STLClient(server = "localhost")

try:

    c.connect()
    c.reset(ports = [0])

    # iterate over the list and send each file to the server
    for pcap_file in pcap_file_list:
        pcap_file = os.path.abspath(pcap_file)

        c.push_remote(pcap_file = pcap_file,
                      ports = 0,
                      ipg_usec = 100,
                      count = 1)

        c.wait_on_traffic()


        stats = c.get_stats()
        opackets = stats[port]['opackets']
        print("{0} packets were Tx on port {1}\n".format(opackets, port))

  except STLError as e:
      print(e)
      sys.exit(1)

  finally:
      c.disconnect()

2.14. Performance Tweaking

In this section we provide some advanced features to help get the most of TRex performance. The reason that those features are not active out of the box because they might have some impact on other areas and in general, might sacrafice one or more properties that requires the user to explicitly give up on those.

2.14.1. Caching MBUFs

see here

2.14.2. Core masking per interface

By default, TRex will regard any TX command with a greedy approach: All the DP cores associated with this port will be assigned in order to produce the maximum throughput.

Figure 22. Greedy Approach - Splitting

However, in some cases it might be beneficial to provide a port with a subset of the cores to use.

For example, when injecting traffic on two ports and the following conditions are met:

  • the two ports are adjacent

  • the profile is symmetric

Due to TRex architecture, adjacent ports (e.g. port 0 & port 1) shares the same cores, and using the greedy approach will cause all the cores to transmit on both port 0 and port 1.

When the profile is symmetric it will be wiser to pin half the cores to port 0 and half the cores to port 1 and thus avoid cache trashing and bouncing. If the profile is not symmetric, the static pinning may deny CPU cycles from the more congested port.

Figure 23. Pinning Cores To Ports

TRex provides this in two ways:

2.14.3. Predefind modes

As said above, the default mode is split mode, but you can provide a predefined mode called pin. This can be done by both API and from the console:


trex>start -f stl/syn_attack.py -m 40mpps --total -p 0 1 --pin        <-- provide '--pin' to the command

Removing all streams from port(s) [0, 1]:                    [SUCCESS]


Attaching 1 streams to port(s) [0]:                          [SUCCESS]


Attaching 1 streams to port(s) [1]:                          [SUCCESS]


Starting traffic on port(s) [0, 1]:                          [SUCCESS]

60.20 [ms]

trex>
API example to PIN cores
 c.start(ports = [port_a, port_b], mult = rate,core_mask=STLClient.CORE_MASK_PIN) 1
1 core_mask = STLClient.CORE_MASK_PIN
API example to MASK cores
 c.start(ports = [port_a, port_b], mult = rate, core_mask=[0x1,0x2])1
1 DP Core 0 (mask==1) is assign to port 1 and DP core 1 (mask==2) is for port 2

We can see in the CPU util. available from the TUI window,
that each core was reserverd for an interface:

Global Stats:

Total Tx L2  : 20.49 Gb/sec
Total Tx L1  : 26.89 Gb/sec
Total Rx     : 20.49 Gb/sec
Total Pps    : 40.01 Mpkt/sec       <-- performance meets the requested rate
Drop Rate    : 0.00 b/sec
Queue Full   : 0 pkts


Cpu Util(%)

  Thread   | Avg | Latest | -1  | -2  | -3  | -4  | -5  | -6  | -7  | -8

 0   (0)   |  92 |     92 |  92 |  91 |  91 |  92 |  91 |  92 |  93 |  94
 1 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 2   (1)   |  96 |     95 |  95 |  96 |  96 |  96 |  96 |  95 |  94 |  95
 3 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 4   (0)   |  92 |     93 |  93 |  91 |  91 |  93 |  93 |  93 |  93 |  93
 5 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 6   (1)   |  88 |     88 |  88 |  88 |  88 |  88 |  88 |  88 |  87 |  87
 7 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0

If we had used the default mode, the table should have looked like this, and yield much worse performance:


Global Stats:

Total Tx L2  : 12.34 Gb/sec
Total Tx L1  : 16.19 Gb/sec
Total Rx     : 12.34 Gb/sec
Total Pps    : 24.09 Mpkt/sec       <-- performance is quite low than requested
Drop Rate    : 0.00 b/sec
Queue Full   : 0 pkts

Cpu Util(%)

  Thread   | Avg | Latest | -1  | -2  | -3  | -4  | -5  | -6  | -7  | -8

 0  (0,1)  | 100 |    100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
 1 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 2  (0,1)  | 100 |    100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
 3 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 4  (0,1)  | 100 |    100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
 5 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 6  (0,1)  | 100 |    100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
 7 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0

This feature is also available from the Python API by providing: CORE_MASK_SPLIT or CORE_MASK_PIN to the start API.

2.14.4. Manual mask

Sometimes for debug purposes or for a more advanced core scheduling you might want to provide a manual masking that will guide the server on which cores to use.

For example, let’s assume we have a profile that utilize 95% of the traffic on one side, and in the other direction it provides 5% of the traffic. Let’s assume also we have 8 cores assigned to the two interfaces.

We want to assign 3 cores to interface 0 and 1 core only to interface 1.

We can provide this line to the console (or for the API by providing a list of masks to the start command):

trex>start -f stl/syn_attack.py -m 10mpps --total -p 0 1 --core_mask 0xE 0x1

Removing all streams from port(s) [0, 1]:                    [SUCCESS]


Attaching 1 streams to port(s) [0]:                          [SUCCESS]


Attaching 1 streams to port(s) [1]:                          [SUCCESS]


Starting traffic on port(s) [0, 1]:                          [SUCCESS]

37.19 [ms]

trex>
 c.start(ports = [port_a, port_b], mult = rate,core_mask=[0x0xe,0x1]) 1
1 mask of cores per port

The following output is received on the TUI CPU util window:


Total Tx L2  : 5.12 Gb/sec
Total Tx L1  : 6.72 Gb/sec
Total Rx     : 5.12 Gb/sec
Total Pps    : 10.00 Mpkt/sec
Drop Rate    : 0.00 b/sec
Queue Full   : 0 pkts

Cpu Util(%)

  Thread   | Avg | Latest | -1  | -2  | -3  | -4  | -5  | -6  | -7  | -8

 0   (1)   |  45 |     45 |  45 |  45 |  45 |  45 |  46 |  45 |  46 |  45
 1 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 2   (0)   |  15 |     15 |  14 |  15 |  15 |  14 |  14 |  14 |  14 |  14
 3 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 4   (0)   |  14 |     14 |  14 |  14 |  14 |  14 |  14 |  14 |  15 |  14
 5 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0
 6   (0)   |  15 |     15 |  15 |  15 |  15 |  15 |  15 |  15 |  15 |  15
 7 (IDLE)  |   0 |      0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0

2.15. Reference

Additional profiles and examples are available in the stl/hlt folder.

For information about the Python client API, see the Python Client API documentation.

2.16. Console commands

2.16.1. Overview

The console uses TRex client API to control TRex.

Important information about console usage

  • The console does not save its own state. It caches the server state. It is assumed that there is only one console with R/W permission at any given time, so once connected as R/W console (per user/interface), it can read the server state and then cache all operations.

  • Many read-only clients can exist for the same user interface.

  • The console syncs with the server to get the state during connection stage, and caches the server information locally.

  • In case of crash or exit of the console, it will sync again at startup.

  • Command line parameters order is not important.

  • The console can display TRex stats in real time. You can open two consoles simultaneously - one for commands (R/W) and one for displaying statistics (read only).

2.16.2. Ports State

state meaning

IDLE

No streams

STREAMS

Has streams. Not transmitting (did not start transmission, or it was stopped).

WORK

Has streams. Transmitting.

PAUSE

Has streams. Transmission paused.

  IDLE -> (add streams) -> STREAMS (start) -> WORK (stop) -> STREAMS (start)
                                           |   WORK (pause) -> PAUSE (resume )---
                                           |                                     |
                                           |                                     |
                                           --------------------------------------

2.16.3. Common Arguments

Following command line arguments are common to many commands.

Help

You can specify -h or --help after each command to get full description of its purpose and arguments.

Example
trex>streams -h
Port mask

Port mask enables selecting range, or set of ports.

Example
trex><command>   [-a] [--port 1 2 3]  [--port 0xff]  [--port clients/servers]

  port mask :
    [-a]           : all ports
    [--port 1 2 3]  : port 1,2 3
    [--port 0xff]   : port by mask 0x1 for port 0 0x3 for port 0 and 1
Duration

Duration is expressed in seconds, minutes, or hours.

Example
trex><command> [-d 100] [-d 10m] [-d 1h]

  duration:
   -d 100 : Seconds
   -d 10m : Minutes
   -d 1h  : Hours
Multiplier

The traffic profile defines default bandwidth for each stream. Using the multiplier command line argument, it is possible to set different bandwidth. It is possible to specify either packets or bytes per second, percentage of total port rate, or just factor to multiply the original rate by.

Example
trex><command> [-m 100] [-m 10gb] [-m 10kpps] [-m 40%]

  multiplier :

  -m 100    : Multiply original rate by given factor.
  -m 10gbps : From graph calculate the maximum rate as this bandwidth for all streams( for each port )
  -m 10kpps : From graph calculate the maximum rate as this pps for all streams      ( for each port )
  -m 40%    : From graph calculate the maximum rate as this precent from total port rate ( for each port )

2.16.4. Commands

connect

Attempts to connet to the server you were connected to. Can be used in case server was restarted. Can not be used in order to connect to different server. In addition:

  • Syncs the port info and stream info state.

  • Reads all counter statistics for reference.

Example
$connect
reset

Resets the server and client to a known state. Not used in normal scenarios.

  • Forces acquire on all ports

  • Stops all traffic on all ports

  • Removes all streams from all ports

    Example
trex>reset
portattr

Configures port attributes.

Example
trex>portattr --help
usage: port_attr [-h] [--port PORTS [PORTS ...] | -a] [--prom {on,off}]
                 [--link {up,down}] [--led {on,off}] [--fc {none,tx,rx,full}]
                 [--supp]

Sets port attributes

optional arguments:
  -h, --help            show this help message and exit
  --port PORTS [PORTS ...], -p PORTS [PORTS ...]
                        A list of ports on which to apply the command
  -a                    Set this flag to apply the command on all available
                        ports
  --prom {on,off}       Set port promiscuous on/off
  --link {up,down}      Set link status up/down
  --led {on,off}        Set LED status on/off
  --fc {none,tx,rx,full}
                        Set Flow Control type
  --supp                Show which attributes are supported by current NICs
images/console_link_down.png
Figure 24. Setting link down on port 0 affects port 1 at loopback
clear

Clears all port stats counters.

Example
trex>clear -a
stats

Can be used to show global/port/stream statistics.
Also, can be used to retrieve extended stats from port (xstats)

Example
trex>stats --port 0 -p
trex>stats -s
Xstats error example

trex>stats -x --port 0 2
Xstats:

            Name:              |     Port 0:     |     Port 2:
\------------------------------------------------------------------
rx_good_packets                |       154612905 |       153744994
tx_good_packets                |       154612819 |       153745136
rx_good_bytes                  |      9895225920 |      9839679168
tx_good_bytes                  |      9276768500 |      9224707392
rx_unicast_packets             |       154611873 |       153743952
rx_unknown_protocol_packets    |       154611896 |       153743991
tx_unicast_packets             |       154612229 |       153744562
mac_remote_errors              |               1 |               0 #1
rx_size_64_packets             |       154612170 |       153744295
tx_size_64_packets             |       154612595 |       153744902
1 Error that can be seen only with this command
streams

Shows info about configured streams on each port, from the client cache.

Example
trex>streams

Port 0:

    ID     |     packet type     |  length  |       mode       |      rate       | next stream

    1      | Ethernet:IP:UDP:Raw |       64 |    continuous    |           1 pps |      -1
    2      | Ethernet:IP:UDP:Raw |       64 |    continuous    |       1.00 Kpps |      -1

Port 1:

    ID     |     packet type     |  length  |       mode       |      rate       | next stream

    1      | Ethernet:IP:UDP:Raw |       64 |    continuous    |           1 pps |      -1
    2      | Ethernet:IP:UDP:Raw |       64 |    continuous    |       1.00 Kpps |      -1
Example

Use this command to show only ports 1 and 2.

trex>streams --port 1 2

 ..
 ..
Example

Use this command to show full information for stream 0 and port 0, output in JSON format.

trex>streams --port 0 --streams 0
start

Start transmitting traffic on set of ports

  • Removes all streams

  • Loads new streams

  • Starts traffic (can set multiplier, duration and other parameters)

  • Acts only on ports in "stopped: mode. If --force is specified, port(s) are first stopped.

  • Note: If any ports are not in "stopped" mode, and --force is not used the command fails.

Example

Use this command to start a profile on all ports, with a maximum bandwidth of 10 GB.

trex>start -a -f stl/imix.py  -m 10gb
Example

Use this command to start a profile on ports 1 and 2, and multiply the bandwidth specified in the traffic profile by 100.

trex>start -port 1 2 -f stl/imix.py  -m 100
stop
  • Operates on a set of ports

  • Changes the mode of the port(s) to "stopped"

  • Does not remove streams

Example

Use this command to stop the specified ports.

trex>stop --port 0
pause
  • Operates on a set of ports

  • Changes a working set of ports to "pause" (no traffic transmission) state.

    Example
trex>pause --port 0
resume
  • Operates on a set of ports

  • Changes a working set of port(s) to "resume" state (transmitting traffic again).

  • All ports should be in "paused" status. If any of the ports is not paused, the command fails.

Example
trex>resume --port 0
update

Update the bandwidth multiplier for a set of ports.

  • All ports must be in "work" state. If any ports are not in "work" state, the command fails

Example

Multiplly traffic on all ports by a factor of 5.

trex>update -a -m 5
Note
We might add in the future the ability to disable/enable specific stream, load a new stream dynamically, and so on.
TUI

The textual user interface (TUI) displays constantly updated TRex statistics in a textual window.

Example
trex>tui

Enters a Stats mode and displays three types of TRex statistics: * Global/port stats/version/connected etc * Per port * Per port stream

The followig keyboard commands operate in the TUI window:
q - Quit the TUI window (get back to console)
c - Clear all counters
d, s, l - change display between dashboard (d), streams (s) and l (latency) info.

2.17. Benchmarks of 40G NICs

2.18. Appendix

2.18.1. Scapy packet examples


# UDP header
Ether()/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)

# UDP over one vlan
Ether()/Dot1Q(vlan=12)/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)

# UDP QinQ
Ether()/Dot1Q(vlan=12)/Dot1Q(vlan=12)/IP(src="16.0.0.1",dst="48.0.0.1")/UDP(dport=12,sport=1025)

#TCP over IP over VLAN
Ether()/Dot1Q(vlan=12)/IP(src="16.0.0.1",dst="48.0.0.1")/TCP(dport=12,sport=1025)

# IPv6 over vlan
Ether()/Dot1Q(vlan=12)/IPv6(src="::5")/TCP(dport=12,sport=1025)

#Ipv6 over UDP over IP
Ether()/IP()/UDP()/IPv6(src="::5")/TCP(dport=12,sport=1025)

#DNS packet
Ether()/IP()/UDP()/DNS()

#HTTP packet
Ether()/IP()/TCP()/"GET / HTTP/1.1\r\nHost: www.google.com\r\n\r\n"

2.18.2. HLT supported Arguments

connect
Argument Default Comment

device

localhost

ip or hostname of TRex

port_list

None

list of ports

username

TRexUser

reset

True

break_locks

False

cleanup_session
Argument Default Comment

maintain_lock

False

release ports at the end or not

port_list

None

port_handle

None

traffic_config
Argument Default Comment

mode

None

( create | modify | remove | reset )

split_by_cores

split

( split | duplicate | single ) TRex extention: split = split traffic by cores, duplicate = duplicate traffic for all cores, single = run only with sinle core (not implemented yet)

load_profile

None

TRex extention: path to filename with stream profile (stream builder parameters will be ignored, limitation: modify)

consistent_random

False

TRex extention: False (default) = random sequence will be different every run, True = random sequence will be same every run

ignore_macs

False

TRex extention: True = use MACs from server configuration, no MAC VM (workaround on lack of ARP)

disable_flow_stats

False

TRex extention: True = don’t use flow stats for this stream, (workaround for limitation on type of packet for flow_stats)

flow_stats_id

None

TRex extention: uint, for use of STLHltStream, specifies id for flow stats (see stateless manual for flow_stats details)

port_handle

None

port_handle2

None

bidirectional

False

stream builder parameters

transmit_mode

continuous

( continuous | multi_burst | single_burst )

rate_pps

None

rate_bps

None

rate_percent

10

stream_id

None

name

None

direction

0

TRex extention: 1 = exchange sources and destinations, 0 = do nothing

pkts_per_burst

1

burst_loop_count

1

inter_burst_gap

12

length_mode

fixed

( auto | fixed | increment | decrement | random | imix )

l3_imix1_size

64

l3_imix1_ratio

7

l3_imix2_size

570

l3_imix2_ratio

4

l3_imix3_size

1518

l3_imix3_ratio

1

l3_imix4_size

9230

l3_imix4_ratio

0

L2

frame_size

64

frame_size_min

64

frame_size_max

64

frame_size_step

1

l2_encap

ethernet_ii

( ethernet_ii | ethernet_ii_vlan )

mac_src

00:00:01:00:00:01

mac_dst

00:00:00:00:00:00

mac_src2

00:00:01:00:00:01

mac_dst2

00:00:00:00:00:00

mac_src_mode

fixed

( fixed | increment | decrement | random )

mac_src_step

1

mac_src_count

1

mac_dst_mode

fixed

( fixed | increment | decrement | random )

mac_dst_step

1

mac_dst_count

1

mac_src2_mode

fixed

( fixed | increment | decrement | random )

mac_src2_step

1

mac_src2_count

1

mac_dst2_mode

fixed

( fixed | increment | decrement | random )

mac_dst2_step

1

mac_dst2_count

1

vlan options below can have multiple values for nested Dot1Q headers

vlan_user_priority

1

vlan_priority_mode

fixed

( fixed | increment | decrement | random )

vlan_priority_count

1

vlan_priority_step

1

vlan_id

0

vlan_id_mode

fixed

( fixed | increment | decrement | random )

vlan_id_count

1

vlan_id_step

1

vlan_cfi

1

vlan_protocol_tag_id

None

L3, general

l3_protocol

None

( ipv4 | ipv6 )

l3_length_min

110

l3_length_max

238

l3_length_step

1

L3, IPv4

ip_precedence

0

ip_tos_field

0

ip_mbz

0

ip_delay

0

ip_throughput

0

ip_reliability

0

ip_cost

0

ip_reserved

0

ip_dscp

0

ip_cu

0

l3_length

None

ip_id

0

ip_fragment_offset

0

ip_ttl

64

ip_checksum

None

ip_src_addr

0.0.0.0

ip_dst_addr

192.0.0.1

ip_src_mode

fixed

( fixed | increment | decrement | random )

ip_src_step

1

ip or number

ip_src_count

1

ip_dst_mode

fixed

( fixed | increment | decrement | random )

ip_dst_step

1

ip or number

ip_dst_count

1

L3, IPv6

ipv6_traffic_class

0

ipv6_flow_label

0

ipv6_length

None

ipv6_next_header

None

ipv6_hop_limit

64

ipv6_src_addr

fe80:0:0:0:0:0:0:12

ipv6_dst_addr

fe80:0:0:0:0:0:0:22

ipv6_src_mode

fixed

( fixed | increment | decrement | random )

ipv6_src_step

1

we are changing only 32 lowest bits; can be ipv6 or number

ipv6_src_count

1

ipv6_dst_mode

fixed

( fixed | increment | decrement | random )

ipv6_dst_step

1

we are changing only 32 lowest bits; can be ipv6 or number

ipv6_dst_count

1

L4, TCP

l4_protocol

None

( tcp | udp )

tcp_src_port

1024

tcp_dst_port

80

tcp_seq_num

1

tcp_ack_num

1

tcp_data_offset

5

tcp_fin_flag

0

tcp_syn_flag

0

tcp_rst_flag

0

tcp_psh_flag

0

tcp_ack_flag

0

tcp_urg_flag

0

tcp_window

4069

tcp_checksum

None

tcp_urgent_ptr

0

tcp_src_port_mode

increment

( increment | decrement | random )

tcp_src_port_step

1

tcp_src_port_count

1

tcp_dst_port_mode

increment

( increment | decrement | random )

tcp_dst_port_step

1

tcp_dst_port_count

1

L4, UDP

udp_src_port

1024

udp_dst_port

80

udp_length

None

udp_dst_port_mode

increment

( increment | decrement | random )

udp_src_port_step

1

udp_src_port_count

1

udp_src_port_mode

increment

( increment | decrement | random )

udp_dst_port_step

1

udp_dst_port_count

1

traffic_control
Argument Default Comment

action

None

( clear_stats | run | stop | sync_run | poll | reset )

port_handle

None

traffic_stats
Argument Default Comment

mode

aggregate

( all | aggregate | streams )

port_handle

None

2.18.3. FD.IO open source project using TRex

2.18.4. Using Stateless client via JSON-RPC

For functions that do not require complex objects and can use JSON-serializable input/output, you can use Stateless API via JSON-RPC proxy server.
Thus, you can use Stateless TRex from any language supporting JSON-RPC.

How to run TRex side:
  • Run the Stateless TRex server in one of 2 ways:

    • Either run TRex directly in shell:

      [bash]>sudo ./t-rex-64 -i
    • Or run it via JSON-RPC command to trex_daemon_server:

      start_trex(trex_cmd_options, user, block_to_success = True, timeout = 40, stateless = True)
  • Run the RPC "proxy" to stateless, here are also 2 ways:

    • run directly:

      cd automation/trex_control_plane/stl/examples
      python rpc_proxy_server.py
    • Send JSON-RPC command to master_daemon:

      if not master_daemon.is_stl_rpc_proxy_running():
          master_daemon.start_stl_rpc_proxy()

Done :)

Now you can send requests to the rpc_proxy_server and get results as array of 2 values:

  • If fail, result will be: [False, <traceback log with error>]

  • If success, result will be: [True, <return value of called function>]

In same directory of rpc_proxy_server.py, there is python example of usage: using_rpc_proxy.py

Native Stateless API functions:
  • acquire

  • connect

  • disconnect

  • get_stats

  • get_warnings

  • push_remote

  • reset

  • wait_on_traffic

…can be called directly as server.push_remote('udp_traffic.pcap').
If you need any other function of stateless client, you can either add it to rpc_proxy_server.py, or use this method:
server.native_method(<string of function name>, <args of the function>)

HLTAPI Methods can be called here as well:
  • connect

  • cleanup_session

  • interface_config

  • traffic_config

  • traffic_control

  • traffic_stats

Note

In case of names collision with native functions (such as connect), for HLTAPI, function will change to have "hlt_" prefix.

Example of running from Java:
package com.cisco.trex_example;

import java.net.URL;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.util.HashMap;

import com.googlecode.jsonrpc4j.JsonRpcHttpClient;

public class TrexMain {

    @SuppressWarnings("rawtypes")
    public static Object verify(ArrayList response) {
        if ((boolean) response.get(0)) {
            return response.get(1);
        }
        System.out.println("Error: " + response.get(1));
        System.exit(1);
        return null;
    }

    @SuppressWarnings("rawtypes")
    public static void main(String[] args) throws Throwable {
        try {
            String trex_host = "csi-trex-11";
            int rpc_proxy_port = 8095;
            Map<String, Object> kwargs = new HashMap<>();
            ArrayList<Integer> ports = new ArrayList<Integer>();
            HashMap res_dict = new HashMap<>();
            ArrayList res_list = new ArrayList();
            JsonRpcHttpClient rpcConnection = new JsonRpcHttpClient(new URL("http://" + trex_host + ":" + rpc_proxy_port));

            System.out.println("Initializing Native Client");
            kwargs.put("server", trex_host);
            kwargs.put("force", true);
            verify(rpcConnection.invoke("native_proxy_init", kwargs, ArrayList.class));
            kwargs.clear();

            System.out.println("Connecting to TRex server");
            verify(rpcConnection.invoke("connect", kwargs, ArrayList.class));

            System.out.println("Resetting all ports");
            verify(rpcConnection.invoke("reset", kwargs, ArrayList.class));

            System.out.println("Getting ports info");
            kwargs.put("func_name", "get_port_info"); // some "custom" function
            res_list = (ArrayList) verify(rpcConnection.invoke("native_method", kwargs, ArrayList.class));
            System.out.println("Ports info is: " + Arrays.toString(res_list.toArray()));
            kwargs.clear();
            for (int i = 0; i < res_list.size(); i++) {
                    Map port = (Map) res_list.get(i);
                    ports.add((int)port.get("index"));
                    }

            System.out.println("Sending pcap to ports: " + Arrays.toString(ports.toArray()));
            kwargs.put("pcap_filename", "stl/sample.pcap");
            verify(rpcConnection.invoke("push_remote", kwargs, ArrayList.class));
            kwargs.clear();
            verify(rpcConnection.invoke("wait_on_traffic", kwargs, ArrayList.class));

            System.out.println("Getting stats");
            res_dict = (HashMap) verify(rpcConnection.invoke("get_stats", kwargs, ArrayList.class));
            System.out.println("Stats: " + res_dict.toString());

            System.out.println("Deleting Native Client instance");
            verify(rpcConnection.invoke("native_proxy_del", kwargs, ArrayList.class));

        } catch (Throwable e) {
            e.printStackTrace();
        }
    }
}