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mm-live

Live plotting for the mahimahi link emulator. Mahimahi comes with a very nice native live plotting system already, but unfortunately it does not work over ssh and is not easily configurable. This system fills that void by creating a server that any number of clients can connect to simultaneously through their web browser. The server pushes the raw throughput and delay data to the clients using websockets and the browser plots the data using d3.

Dependencies

  • node.js / npm (tested with v8/v5, but should work with others as well)
  • python
  • mahimahi

Install

If you have all the required dependencies installed already, installing mm-live should be as simple as running make in the root of this repository.

The Makefile defaults to placing the executable in /usr/local/bin, which usually requires root. If you'd like to install it elsewhere, just change the destination directory in the Makefile (and ensure this destination directory is included in your PATH environment variable).

Usage

To live plot the throughput and delay of any mahimahi shell, simply add mm-live as the outer-most shell.

mm-live takes two required arguments before the inner shells:

  1. "uplink" or "downlink" -- the direction of traffic to monitor in the graph
  2. server-port (integer) -- starts the live plotting server on this port

For example to emulate a link with 100ms RTT, 48Mbps bandwidth, and 1BDP droptail buffers, and start monitoring the uplink on port 8080, run:

mm-live uplink 8080 mm-delay 50 mm-link --uplink-queue="droptail" --uplink-queue-args="packets=400"

Then simply point your web-browser to http://[IP]:8080.

The server output log is stored in the current directory at server.[server-port].log by default. If the visualization is not displaying, you should check here for any errors.

For more information, run mm-live --help

Demo

Demo

Current Limitations

  • Axes are fixed and do not adjust automatically adjust to zoom as values change.
  • Only uplink or downlink can be monitored at a given time, not both
  • Only supports aggregate throughput, cannot monitor individual flows

(none of these limitations are fundamental so I hope to fix them in the future)