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TCP Experiment Automation Controlled Using Python (TEACUP) -- Documentation

DOCUMENTATION

This page gives an overview of TEACUP's capabilities, traffic generators, information loggers, the configuration file, how to run experiment, and how to analyse the results (based on v0.9). More comprehensive usage information for v1.0 can be found in the following CAIA technical reports:

Note that on June 9th 2016 the source files for the above three technical reports were released under a CC BY-SA 4.0 license and then committed to TEACUP's Sourceforge repository

Overview

TEACUP is a software tool for running TCP performance experiments in a controlled physical testbed.  The typical use-case involves TEACUP controlling a private testbed containing multiple hosts either side of a Linux-based bottleneck router in the classic dumbbell topology. TEACUP itself runs on a separate control host, orchestrating the configuration of end hosts and bottleneck router as required for particular experiments or range of experiments. (The following technical report, CAIA Testbed for TEACUP Experiments Version 2, describes the specific testbed we used in-house while developing TEACUP.)

Experiments with multiple permutations

TEACUP utilises a configuration file to define experiments as combinations of parameters specifying network path and end host conditions. When multiple values are provided (e.g. for the bottleneck rate limit, or TCP congestion control algorithm), an experiment is made up of multiple tests -- consecutively run instances of the experiment run for each parameter combination.  For each experiment and test, TEACUP collects a range of data, such as tcpdump files of traffic seen at all network interfaces, FreeBSD SIFTR logs and Linux Web10G logs. TEACUP also collects a variety of metadata from the end hosts and bottleneck router (such as the actual OS/kernel version(s) in use, network interface configuration, NTP state and so forth).

Based on Python & Fabric

TEACUP is build on the Python Fabric toolkit. Fabric is a Python (2.5 or higher) library and command line tool for remote application deployment or system administration tasks using SSH. Fabric provides several basic operations for executing local or remote shell commands and uploading/downloading files, as well as auxiliary functions, such as prompting the user for input, or aborting execution of the current task.

TEACUP is implemented as a number of Fabric tasks as well as auxiliary functions. TEACUP tasks can then be executed directly from the command line using the Fabric tool fab. The entry point is a file commonly named fabfile.py, typically located in a directory from which we execute Fabric tasks. For TEACUP this is the directory under which we will store the experiment results. The command 'fab -l' lists all available TEACUP tasks. The behaviours of many TEACUP tasks are modified by additional string parameters supplied on the command line.

Testbed environment

TEACUP currently only support a topology where we have one bottleneck router with two testbed network interfaces (NICs) connected to two testbed subnets. Hosts in either subnet act as traffic sources and sinks. In the future we plan to extend TEACUP to support multiple routers and more than two subnets.

The following list describes what TEACUP can control/select on an appropriately configured testbed:

  • Host Operating Systems (OS): Currently TEACUP supports FreeBSD, Linux, Windows 7 (with Cygwin), and Mac OS X
  • OS-specific TCP algorithms: Depending on the OS in each host, TEACUP currently supports the selection and use of NewReno and CUBIC under Linux or FreeBSD (representing classic loss-based algorithms), CompoundTCP (Microsoft’s hybrid), CDG (CAIA’s hybrid under FreeBSD), and HD (Hamilton Institutes’ delay-based TCP under FreeBSD)
  • Path characteristics: TEACUP allows to configure bottleneck bandwidth limits to represent likely consumer experience (e.g. ADSL), and some data centre scenarios. It also supports emulation of constant path delay and loss in either direction to simulate different conditions between traffic sources and sinks. The emulation is implemented by the bottleneck node (router)
  • Bottleneck AQM: TEACUP can select from the Active Queuing Management (AQM) mechanisms supported by the Linux kernel, such as Tail- Drop/FIFO, CoDel, FQ CoDel, PIE, RED, or the FreeBSD kernel, such as FIFO or RED
  • ECN Support: TEACUP can enable/disable Explicit Congestion Notification (ECN) on hosts and/or the router
  • Traffic loads: Traffic generators exist to synthesise the following traffic types:
    • Streaming media over HTTP/TCP (DASH-like)
    • TCP bulk transfer (iperf)
    • UDP flows (VoIP-like)
    • Data centre query/response patterns (one query to N responders)

Installation

Please refer to the INSTALL file in the TEACUP distribution on how to install TEACUP.

Time Synchronisation

TEACUP assumes that all hosts participating in an experiment are time synchronised (for example, by using NTP) so we can align data in the time domain that was collected across multiple hosts. At the start of an experiment TEACUP checks every host clock, and aborts if any clock differs by a user definable threshold (specified in seconds).

Techniques like NTP may not fully synchronise a host's clock soon after rebooting, so TEACUP also supports an optional mechanism for correcting timestamps after the fact. When enabled, TEACUP broadcasts or multicasts ICMP pings at regular intervals throughout an experiment. These packets are sent over the testbed's separate control network, and received by each host on their control network NIC. Assuming the latency variance of the network (i.e. sender NIC, switch, receiver NIC) is small these ICMP packets arrive at all hosts essentially simultaneously. Their arrival times can be used to estimate, and correct for, any clock offset that may still exist between testbed hosts during each experiment.

Experiment Process Flow

For each series of experiments TEACUP will carry out the following steps:
  1. Initialise and check config file
  2. Get parameter combination for next experiment
  3. Start experiment based on config and parameter configuration
  4. If there is another parameter combination to run go to step 3, otherwise finish
The main step is step 3, which can be separated into the following steps:
  1. Log experiment test ID in file experiments_started.txt
  2. Get host information: OS, NIC names, NIC MAC addresses
  3. Reboot hosts: reboot hosts as required given the configuration
  4. Topology configuration: put hosts in subnet configured
  5. Get host information again: OS, NIC names, NIC MAC addresses
  6. Run sanity checks, e.g. check that tools to be used exist
  7. Initialise hosts, e.g. configure network interfaces
  8. Configure router queues based on configuration
  9. Log host state: log host information
  10. Start all logging processes, e.g. tcpdump
  11. Start all traffic generators
  12. Wait for experiment to finish
  13. Stop all running processes on hosts
  14. Collect all log files from logging and traffic generating processes

Traffic Generators

TEACUP can generate the following types of traffic.
  1. TCP bulk transfer
  2. ping
  3. HTTP traffic
  4. DASH-like HTTP video streaming traffic
  5. One querier, N responders (incast) traffic
  6. Unidirectional UDP flows with fixed rate
  7. VoIP-like flows (constant bit rate)

The tool iperf is used to generate TCP bulk transfer flows. Note that the iperf client pushes data to the iperf server, so the data flows in the opposite direction compared to httperf. iperf is also used to generate unidirectional UDP flows with a specified bandwidth and two iperfs can be combined to generate bidirectional UDP flows.

A modified httperf is used to simulate an HTTP client. It can generate simple request patterns, such as accessing some .html file n times per second. It can also generate complex workloads based on work session log files (c.f. httperf man page). httperf is also used to emulate a TCP video streaming session. The httperf client emulates the behaviour of DASH or other similar TCP streaming algorithms. In the initial buffering phase the client will download the first part of the content as fast as possible. Then the client will fetch another block of content every t seconds. The video rate and the cycle length are configurable (and the size of the data blocks depends on these). httperf is also used to emulate the incast scenario. The httperf client will request a block of content from n servers every t seconds. The requests are sent as close together as possible to make sure the servers respond simultaneously. The size of the data blocks is configurable.

As web server TEACUP uses lighttpd. TEACUP automatically sets up fake content for DASH-like video streaming and incast scenario traffic. However, for specific experiments one may need to setup web server content manually or create new scripts to do this.

The tool nttcp is used to emulate simple constant bit rate UDP VoIP flows. The fixed packet size and inter-packet time can be configured.

Information Loggers

All traffic during an experiment is logged with tcpdump and TCP state information is logged with different OS-specific tools.

The tool tcpdump is used to capture the traffic on all testbed NICs on all hosts including the router. Different tools are used to log TCP state information on all hosts except the router. On FreeBSD SIFTR is used. On Linux Web10G is used, which implements the TCP EStats MIB (RFC 4898) inside the Linux kernel. We implemented our own logging tool based on the Web10G library, which is now also in the official Web10G code distribution.

For Windows 7 we implemented our own logging tool, which can access the TCP EStats MIB inside the Windows 7 kernel and logs to a Web10G-compatible format. For Mac OS X we implemented our own logging tool based on DTRACE, which logs in SIFTR-compatible format.

The statistics collected by SIFTR are described in the SIFTR README. The statistics collected by our Web10G client and the Windows 7 EStats logger are identical (based on the Web10G statistics) and are described as part of the web100 (predecessor of Web10G) documentation.

TEACUP also logs the output of traffic generators. In addition it collects per-host information. The following information is gathered before an experiment is started.

  • Output of ifconfig (FreeBSD/Linux/Mac) or ipconfig (Windows)
  • Output of uname -a
  • Information about routing obtained with netstat -r
  • Information about the NTP status based on ntpq -p
  • List of all running processes (output of ps).
  • Output of sysctl -a (FreeBSD/Linux/Mac) and various information for Windows.
  • Information about all of TEACUP's V_ parameters in config.py
  • Information of the TCP congestion control algorithm used on each host, and any TCP parameter settings specified
  • TCP congestion control kernel module parameter settings (Linux only)
  • Network interface configuration information provided by ethtool (Linux only)
The following information is collected after an experiment has finished.
  • Information about the router queue setup (including all queue discipline parameters) and router queue and filtering statistics based on the output of tc (Linux) or ipfw (FreeBSD).

Log File Naming

All log files generated by TEACUP adhere to the following naming scheme.

<test_ID_pfx>_[<par_name>_<par_val>_]*_<host>_ [<traffgen_ID>_]_<file_name>.<extension>.gz

The test ID prefix <test_ID_pfx> is the start of the file name and either specified in the config file (TPCONF_test_id) or on the command line.

The [<par_name>_<par_val>_]* is the zero to n parameter names and parameter values (separated by an underscore). Parameter names (<par_name>) should not contain underscores by definition and all underscores in parameter values (<par_val>) are changed to hyphens (this allows later parsing of the names and values using the underscores as separators). An experiment may have zero parameter names and values. If an experiment was started with run_experiment_multiple there are as many parameters names and values as specified in TPCONF_vary_parameters. We also refer to the part <test_ID_pfx_[<par_name>_<par_val>_]* (the part before the <host>) as test ID.

The <host> part specifies the IP or name of the testbed host a log file was collected from. This corresponds to an entry in TPCONF_router or TPCONF_hosts.

If the log file is from a traffic generator specified in TPCONF_traffic_gens, the traffic generator number follows the host identifier ([<traffgen_ID>]). Otherwise, <traffgen_ID> does not exist. The <file_name> depends on the process which logged the data, for example it set to ‘uname’ for the uname information collected, it is set to ‘httperf_dash’ for an httperf client emulating DASH, or it set to ‘web10g’ for a Web10G log file. tcpdump files are special in that they have an empty file name for tcpdumps collected on hosts (assuming they only have one testbed NIC), or the file name is <int_name>_router for tcpdumps collected on the router (where <int_name> is the name of the NIC, e.g. eth1).

The <extension> is either ‘dmp’ indicating a tcpdump file or ‘log’ for all other log files. All log files are usually gzip’d, hence their file names end with ‘.gz’. The following is an example name for a tcpdump file collected on host testhost2 for an experiment where two parameters (dash, tcp) where varied, and an example name for the output of one httperf traffic generator (traffic generator number 3) executed on host testhost2 for the same experiment.

20131206-170846_windows_dash_1000_tcp_compound_testhost2.dmp.gz
20131206-170846_windows_dash_1000_tcp_compound_testhost2_3_httperf_dash.log.gz

All log files for one experiment or a series of experiments are stored under a sub directory named test_ID_pfx created inside the directory where fabfile.py is located.

Host/Router Setup

The setup of hosts other than the router is relatively straight-forward. First each host is booted into the selected OS. Then hardware offloading, such as TCP segmentation offloading (TSO), is disabled on testbed interfaces (all OS), the TCP host cache is disabled (Linux) or configured with a very short timeout and purged (FreeBSD), and TCP receive and send buffers are set to 2MB or more (FreeBSD, Linux). Next ECN is enabled or disabled depending on the configuration. Then the TCP congestion control algorithm is configured for FreeBSD and Linux (including loading any necessary kernel modules). Then the parameters for the current TCP congestion control algorithm are configured if specified by the user (FreeBSD, Linux). Finally, custom user-specified commands are executed on hosts as specified in the configuration (these can overrule the general setup).

The router setup differs between FreeBSD (where ipfw and Dummynet is used) and Linux (where tc and netem is used). Here we only describe the setup for Linux. We use the term pipe to refer to the virtual pipe an incoming packet traverses before it leaves the router on the other interface. The pipe does the queuing, delay and loss emulation.

First, hardware offloading, such as TCP segmentation offloading (TSO) is disabled on the two testbed interfaces. Then the queuing is configured. We use the following approach. Shaping, AQM and delay/loss emulation is done on the egress NIC (as usual). The hierarchical token bucket (HTB) queuing discipline is used for rate limiting with the desired AQM queuing discipline (e.g. pfifo, codel) as leave node. After rate shaping and AQM constant loss and delay is emulated with netem. For each pipe we set up a new tc class on the two testbed NICs of the router. If pipes are unidirectional a class is only used on one of the two interfaces. Otherwise it is used on both interfaces. In future work we could optimise the unidirectional case and omit the creation of unused classes.

The traffic flow is as follows:

  1. Arriving packets are marked at the netfilter mangle table’s POSTROUTING hook depending on source and destination IP address with a unique mark for each pipe.
  2. Marked packets are classified into the appropriate class based on the mark (a one-to-one mapping between marks and classes) and redirected to a pseudo interface. With pseudo device we refer to the so-called intermediate function block (IFB) device.
  3. The traffic control rules on the pseudo interface do the shaping with HTB (bandwidth as per config) and the chosen AQM (as a leaf queuing discipline).
  4. Packets go back to actual outgoing interface.
  5. The traffic control rules on the actual interface do network delay/loss emulation with netem. We still need classes here to allow for pipe specific delay/loss settings. Hence we use a HTB again, but with the bandwidth set to the maximum possible rate (so there is effectively no rate shaping or AQM here) and netem plus pfifo are used as leaf queuing discipline.
  6. Packets leave the router via stack / network card driver.

The main reason for this setup with pseudo interfaces is to cleanly separate the rate limiting and AQM from the netem delay/loss emulation. One could combine both on one interface, but then there are certain limitation, such as netem must be before the AQM. Also, a big advantage is that with our setup it is possible to emulate different delay or loss for different flows that share the same bottleneck/AQM.

The following figure shows the packet flow assuming in the upstream direction our outgoing interface is eth3 and in the downstream direction our outgoing interface is eth2.


Config File

The TEACUP configuration file is where one defines the parameters of an experiment or a series of experiments. Configuration files are simply Python files that define a number of TPCONF_ parameters. Thus their syntax must comply with the syntax of Python files. On the other hand this allows to use any Python commands or constructs in configuration files.

Here we will only discuss the main sections of a config file. Please refer to the tech report to see an explanation of all the parameters. We also discuss the most commonly used parameters as part of the example usage scenarios.

To iterate over parameter settings for each experiment TEACUP uses V_variables. These are identifiers of the form V_<name>, where <name> must consist of only letters, numbers, hyphens (-) or underscores (_). V_variables can be used in router queue settings, traffic generator settings, TCP algorithm settings or host setup commands. The tech report describes how to define new V_variables.

TEACUP configuration files have multiple sections:

  • Fabric env options (see Fabric documentation);
  • Testbed host definitions;
  • General experiment settings;
  • Router queue configuration;
  • Traffic generator configuration;
  • Parameter ranges;
  • Parameters to vary in a series of experiments.

Unless public key authentication is configured one must define the user name and password for the SSH sessions used to control the remote hosts as Fabric options.

The testbed host definition part must specify the router host and the list of hosts used as traffic sources and sinks. For each host it must also list the IP addresses of the testbed network interfaces.

As part of the general settings one must define the prefix used for all log files, which we also refer to as the test ID prefix. Furthermore, there are a variety of options that control different behaviour, for example one can specify the directory on the remote hosts where the log files are created.

The router queue configuration needs to specify all router queues, their bandwidth, emulated delay and loss rate, AQM technique used, and queue size. Most importantly one must specify the source and destination IP addresses or subnets of the traffic that should traverse the queue.

In the traffic generator section one must define all the traffic generators used, the times the traffic generators are started, and the parameters to be used for the traffic generators.

To be able to iterate over different values of a parameter one must define a set of parameters values for each variable and this set must be mapped to a V_variable. Also one must define all constant parameters. While there are some default parameters, for example for network delay, packet loss rate, bandwidth, TCP algorithm used and so on, the experimenter can define new parameters and V_variables as required.

Finally, one must specify which parameters exactly are varied in a series of experiments and which are kept constant.

Running Experiments

First you should create a new directory for the experiment or series of experiments. Copy the files fabfile.py and run.sh (and run_resume.sh) from the TEACUP distribution into that new directory. Then create a config.py file in the directory (e.g. start with the provided example config.py as a basis and modify it as necessary).

There are two TEACUP tasks to start experiments: run_experiment_single and run_experiment_multiple. To run a single experiment with the default test ID prefix TPCONF_test_id, type:

> fab run_experiment_single

To run a series of experiment based on the TPCONF_ vary_parameters settings with the default test ID prefix TPCONF_test_id, type:
> fab run_experiment_multiple

In both cases the Fabric log output will be printed out on the current terminal and it can be redirected with the usual means. The default test ID prefix TPCONF_test_id is specified in the config file. However, the test ID prefix can also be specified on the command line (overruling the config setting).

For convenience a shell script run.sh shell exists. The shell script logs the Fabric output in a <test_ID_prefix>.log file inside the <test_ID_prefix> sub directory and is started with:

> run.sh

The shell script generates a test ID prefix and then executes the command:
> fab run_experiment_multiple:test_id=<test_ID_pfx> <test_ID_pfx>.log 2>&1

The test ID prefix is set to ‘date +"%Y%m%d-%H%M%S"‘_experiment. The output is unbuffered, so one can use tail -f on the log file and get timely output. The fabfile to be used can be specified, i.e. to use the fabfile myfabfile.py instead of fabfile.py run:
> run.sh myfabfile.py

The run_experiment_single and run_experiment_multiple tasks keeps track of experiments using two files in the current directory:
  • The file experiment_started.txt logs the test IDs of all experiments started.
  • The file experiment_completed.txt logs the test IDs of all experiments successfully completed.
Note that both of these files are never reset by TEACUP. New test IDs are simply appended to the current files if they already exist. It is the user’s responsibility to delete the files in order to reset the list of experiments. It is possible to resume an interrupted series of experiments started with run_experiment_multiple with the resume parameter (see tech report). All experiments of the series that were not completed (not logged in experiment_completed.txt) are done again.

Analysing Experiment Data

TEACUP provides a number of tasks to analyse the data of an experiment or a series of experiments. Here we only describe the basic analysis functions for plotting time series.

Currently analysis functions exist for:

  1. Plotting the throughput including all header bytes (based on tcpdump data);
  2. Plotting the Round Trip Time (RTT) using SPP (based on tcpdump data);
  3. Plotting the TCP congestion window size (CWND) (based on SIFTR and Web10G data);
  4. Plotting the TCP RTT estimate (based on SIFTR and Web10G data). The function can plot both, the smoothed estimate and an unsmoothed estimate (also for SIFTR the unsmoothed estimate is the improved ERTT estimate);
  5. Plotting an arbitrary TCP statistics from SIFTR and Web10G data.

A convenience function exists that plots graphs 1--4 listed above. The easiest way to generate all graphs for all experiments is to run the following command in the directory containing the sub directories with experiment data:

> fab analyse_all

This command will generate results for all experiments listed in the file experiments_completed.txt. By default the TCP RTT graphs generated are for the smoothed RTT estimates and in case of SIFTR this is not the ERTT estimates (if the smoothed parameter is set to ‘0’, nonsmoothed estimates are plotted and in the case of SIFTR this is the ERTT estimates). The analysis can be run for a single experiment only by specifying a test ID. The following command generates all graphs for the experiment 20131206-102931_dash_2000_tcp_newreno:
> fab analyse_all:test_id=20131206-102931_dash_2000_tcp_newreno

One can specify a list of test IDs with the test_id parameter. The test IDs must be separated by semicolons. (If only one test ID is specified, no trailing semicolon is needed.) If multiple IDs are specified the graphs will be created in the sub directory of the test ID specified first. If multiple experiments are plotted on the same graph(s) the file name(s) will be the first test ID specified followed by the string "_comparison" to distinguish from graphs where only one experiment is plotted.

To generate a particular graph for a particular experiment one can use the specific analysis function (analyse_throughput, analyse_spp_rtt, analyse_cwnd, analyse_tcp_rtt) together with a (list of) test ID's) (specifying the test ID(s) is mandatory in this case). For example, the following command only generates the TCP RTT graph for the non-smoothed estimates:

> fab analyse_tcp_rtt:test_id=20131206-102931_dash_2000_tcp_newreno,smoothed=0

Note, the smoothed parameter can also be used with analyse_all. The following command only generates the throughput graph:
> fab analyse_throughput:test_id=20131206-102931_dash_2000_tcp_newreno

The analyse_tcp_stat function can be used to plot any TCP statistic from SIFTR or Web10G logs. For example, we can plot the number of kilo bytes in the send buffer at any given time with the command:

> fab analyse_tcp_stat:test_id=20131206-102931_tcp_newreno,out_dir=./results,siftr_index=22,web10g_index=116, ylabel="Snd buf (kbytes)",yscaler=0.001

The siftr_index defines the index of the column of the statistic to plot for SIFTR log files. The web10g_index defines the index of the column of the statistic to plot for Web10G log files. If one has only SIFTR or only Web10G log files the other index does not need to be specified. But for experiments with SIFTR and Web10G log files both indexes must be specified. By default both indexes are set to plot CWND. The lists of available statistics (including the column numbers) are in the SIFTR README and the Web10G documentation.

Analysis task have a number of parameters that can control the plot behaviour. These are described in the tech report. The analysis functions also have a rudimentary filter mechanism. Note, that this mechanism filters only what is plotted, but not what data is extracted from the log files.

The source_filter parameter indicates the flows to be used to generate data series for plotting. Flows may be specified using combinations of patterns matching source and/or destination IP address and port numbers. The filter string format is:

(S|D)_<ip>_<port>[;(S|D)_<ip>_(<port>|’*’)]*

The following command only plots data for flows from host 172.16.10.2 port 80:
> fab analyse_all:source_filter="S_172.16.10.2_80"

Note, that the notion of flow here is unidirectional. Thus in the above example flows from 172.16.10.2 port 80 are shown, but flows to 172.16.10.2 port 80 are not shown. We can only select flows to host 172.16.10.2 port 80 by specifying:
> fab analyse_all:source_filter="D_172.16.10.2_80"

As a side effect, the specified filter string also determines the order of the flows in the graph(s). Flows are plotted in the order of the filters specified. For example, if there are two flows, one from host 172.16.10.2 port 80 and another from host 172.16.10.2 port 81 by default the port 80 flow would be the first data series and the port 81 flow would be the second data series. One can reverse the two flows in the graphs by specifying:
> fab analyse_all:source_filter="S_172.16.10.2_81;S_172.16.10.2_80"

Instead of an actual port number on can specify the wildcard character (’*’). This allows to filter on a specific source or destination with any port number.

Utility Functions

TEACUP provides a number of utility functions available as Fabric tasks. Here we only list some commands, for a full list please view the tech report. The exec_cmd task can be used to execute one command on multiple hosts. For example, the following command executes the command uname -s on a number of hosts:

> fab -H testhost1,testhost2,testhost3 exec_cmd:cmd="uname -s"

If no hosts are specified on the command line, the exec_cmd command is executed on all hosts listed in the config file (the union set of TPCONF_router and TPCONF_hosts). For example, the following command is executed on all testbed hosts:
> fab exec_cmd:cmd="uname -s"

The copy_file task can be used to copy a local file to a number of testbed hosts. For example, the following command copies the web10g-logger executable to all testbed hosts except the router (this assumes all the hosts run Linux when the command is executed):

> fab -H testhost2,testhost3 copy_file:file_name=/usr/bin/web10g-logger,remote_path=/usr/bin

If no hosts are specified on the command line, the command is executed for all hosts listed in the config file (the union set of TPCONF_router and TPCONF_hosts). For example, the following command copies the file to all testbed hosts:
> fab copy_file:file_name=/usr/bin/web10g-logger,remote_path=/usr/bin

The parameter method controls the method used for copying. By default (method=’put’) copy_file will use the Fabric put function to copy the file. However, the Fabric put function is slow. For large files setting method=’scp’ provides much better performance using the scp command. While scp is faster, it may prompt for the password if public key authentication is not configured.

The init_os task can be used to reboot hosts into specific operating systems (OSs). For example, the following command reboots the hosts testhost1 and testhost2 into the OSs Linux and FreeBSD respectively:

> fab -H testhost1,testhost2 init_os:os_list="Linux\,FreeBSD",force_reboot=1

Note that the commas in os_list need to be escaped with backslashes (\), since otherwise Fabric interprets the commas as parameter delimiters. By default force_reboot is 0, which means hosts that are already running the desired OS are not rebooted. Setting force_reboot to 1 enforces a reboot. By default the script waits 100 seconds for a host to reboot. If the host is not responsive after this time, the script will give up unless the do_power_cycle parameter is set to 1.

The check_host command can be used to check if the required software is installed on the hosts. The task only checks for the presence of necessary tools, but it does not check if the tools actually work. For example, the following command checks all testbed hosts:

> fab -H testhost1,testhost2,testhost3 check_host

The check_connectivity task can be used to check connectivity between testbed hosts with ping. This task only checks the connectivity of the internal testbed network, not the reachability of hosts on their control interface. For example, the following command checks whether each host can reach each other host across the testbed network:
> fab -H testhost1,testhost2,testhost3 check_connectivity

Last Updated: Friday 28-Oct-2016 10:08:47 AEDT | Maintained by: Grenville Armitage (garmitage@swin.edu.au) | Authorised by: Grenville Armitage (garmitage@swin.edu.au)