netperf-wrapper

Python wrapper to run multiple simultaneous netperf/iperf/ping instances and aggregate the results.

Tests are specified as config files (which are really Python), and various parsers for tool output are supplied. At the moment, parsers for netperf in -D mode, iperf in csv mode and ping/ping6 in -D mode are supplied, as well as a generic parser for commands that just outputs a single number.

Several commands can be run in parallel and, provided they output timestamped values, (which netperf ping and iperf do, the latter with a small patch, available in the misc/ directory), the test data points can be aligned with each other in time, interpolating differences between the actual measurement points. This makes it possible to graph (e.g.) ping times before, during and after a link is loaded.

An alternative run mode is running several iterated tests (which each output one data point, e.g. netperf tests not in -D mode), and outputting the results of these several runs.

The aggregated data is saved in (gzipped) json format for later processing and/or import into other tools. The json format is documented below.

Apart from the json format, the data can be output as csv values, emacs org mode tables or plots. Each test can specify several different plots, including time-series plots of the values against each other, as well as CDF plots of (e.g.) ping times.

Plotting requires a functional matplotlib installation (but everything else can run without matplotlib), and can be output to the formats supported by matplotlib by specifying the output filename with -o output.{png,ps,pdf,svg}. If no output file is specified, the plot is diplayed using matplotlib's interactive plot browser, which also allows saving of the output (in .png format).

The basic invocation is ./netperf-wrapper -H . Various options to control test parameters are available; try running ./netperf-wrapper -h. Tests can be displayed with ./netperf-wrapper --list-tests and the available plots can be displayed with ./netperf-wrapper --list-plots .

Running tests and plotting/displaying the output is logically split up in two separate processes, but can be combined into one. When a test is run, its data output is always saved in a file called -.json.gz in the same directory as the output file selected with -o (or the current directory if no output file is selected). This file can be read back in with the -i switch, in which case the test will not be run again, but the saved test data will be used as input for plotting functions etc. If an output format is selected while a test is run, the test data will be used directly for this output, but will still be saved in the json file.

Installing

Install the package system-wide by running sudo python2 setup.py install or sudo pip install netperf-wrapper for the latest released version.

The json data format

The aggregated test data is saved in a file called -.json.gz. This file contains the data points generated during the test, as well as some metadata. The top-level json object has three keys in it: x_values, results and metadata.

x_values is an array of the x values for the test data (typically the time values for timeseries data).

results is a json object containing the result data series. The keys are the data series names; the value for each key is an array of y values for that data series. The data array has the same length as the x_values array, but there may be missing data points (signified by null values).

metadata is an object containing various data points about the test run. The metadata values are read in as configuration parameters when the data set is loaded in for further processing. Not all tests use all the parameters, but they are saved anyway.

Currently the metadata values are:

• NAME: The test name.
• TITLE: Any extra title specified by the -t parameter when the test was run.
• HOST: The server hostname connected to during the test.
• LOCAL_HOST: The hostname of the machine that ran the test.
• LENGTH: Test length in seconds, as specified by the -l parameter.
• TOTAL_LENGTH: Actual data series length, after the test has added time to the LENGTH.
• STEP_SIZE: Time step size granularity.
• TIME: ISO timestamp of the time the test was initiated.