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pensieve_browser
README.md

README.md

Lightweight Deployment

In this case, we provide the original implementation of Pensieve [SIGCOMM'17] and AuTO [SIGCOMM'18] and the implementation provided by TranSys.

Steps to Implement Pensieve Model (./pensieve_browser/)

We implement Pensieve into JavaScript with Tensorflow.js.

Prerequisites

You need to first install node.js and grunt. In the ./dash.js/ directory,

sudo apt install npm nodejs
npm install
npm install -g grunt-cli
grunt

Original Implementations

  • First, you need to obtain the frozen graph model .pb of the DNN. You can utilize the freeze_graph to convert the .ckpt checkpoint to .pb frozen graph.
  • Put the frozen graph into ./pensieve_browser/video_server/. We provide two examples (one hidden layer and five hidden layers) in the folder.

Lightweightified Implementations

  • Convert the DecisionTreeClassifier into JavaScript codes with sklearn-porter (https://github.com/nok/sklearn-porter).
  • Replace the JS codes in dash.js/src/streaming/controllers/ViperDecisionTree.js with the converted JS codes.
  • Run the Gruntfile under dash.js/ to generate a new dash.all.min.js. Note that you may need to add the --force option to ignore spelling warnings.
  • The dash.all.min.js could be found at dash.js/dist/. Put the dash.all.min.js to ./video_server.
  • Move the ./video_server/ to /var/www/html/.
  • Visit the http://localhost/myindex_XX.html (XX should be the name of the ABR). The memory and latency statistics will be displayed on the web page.

Steps to Implement AuTO Model

We adopt the original codes provided by the authors of AuTO at [https://bitbucket.org/JustinasLingys/auto_sigcomm2018/]. The codes for AuTO is still under refactoring.

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