# Pantheon: the training ground for Internet congestion-control research

### Talk 

* Congestion control
  * Cornerstone problem in computer networking 
    * Avoids congestion collapse 
    * Allocates resources among users
    * Affects every application using TCP socket 
  * BBR, Sprout, PCC 

![](/files/3atY6LrtcjJ46YDbCNGC)

* Every emerging algorithm claims to be the SOTA
  * Compared with other algorithms that they picked 
    * Must acquire, compile, and execute prior algorithms 
  * Evaluated on their own testbed 
    * Large service operators: risky to deploy, long turnaround time 
    * Researchers: on a much smaller scales, results may not generalize 
  * On simulators / emulators with their settings
    * How to configure the settings? 
  * Based on specific results they collected 
    * The internet is diverse and evolving 

Other fields:

* Database: TPC 
* Computer systems: SPEC 
* CV: ImageNet 
* Lesson: shared, reproducible benchmarks can lead to huge leaps performance and transform technologies by making them scientific 

Pantheon: a community resource

* A common language in CC
  * Benchmark algorithms
  * Shared testbeds
  * Public data
* A training ground for congestion control 
  * Enables faster innovation and more reproducible research
  * e.g. Vivace (NSDI '18), Copa (NSDI '18), Indigo: a ML-based congestion control 
* 15+ algorithms
* Common testing interface
* Measure performance faithfully without modifications 
  * Performance varies across types of network path, path direction, and time 
* Limitation
  * Only tests schemes at full throttle
  * Nodes are not necessarily representative 
  * Does not measure interactions between different schemes (fairness, TCP-friendliness) 
* Calibrated emulators and pathological emulators 
  * Simulator / emulator: reproducible and allows rapid experimentation 
  * Open problem: what is the choice of parameter values to faithfully emulate a particular target network 
  * Replication errors 
    * Five parameters: a bottleneck link rate, a constant propagation delay, a DropTail threshold for the sender's queue, a loss rate, a bit that selects constant rate or Poisson-governed rate 
  * Steps 
    * Collect a set of results over a particular network path on Pantheon
      * Avg throughput and 95th percentile delay of a dozen algorithms 
    * Run Bayesian optimization 
      * Run twice: constant rate and Poisson-governed rate
      * Objective function f(x): mean replication error
      * Prior: Guassian process
      * Acquisition function: expected improvements 
    * Pathological emulators
      * Very small buffer sizes
      * Severe ACK aggregation
      * Token-bucket policers 
* Ongoing projects: Vivace, Copa, and more; Indigo 
  * Vivace: validating a new scheme in the real world
  * Copa: iterative design with measurements 
  * Indigo: a machine learning design enabled by Pantheon 
    * Model the problem as a sequential decision making problem 
    * Sender observes CC signal at every step, and then it takes an action to adjust the CC window 
    * Goal: learn the mapping from state to action, and encode the mapping into a model 
    * Design
      * State: queueing delay, sending rate, receiving rate, window size, previous action 
      * Model: 1-layer LSTM network (for history) 
    * CC-oracle
      * Outputs an action that brings congestion window closest to the ideal size 
      * Ideal size 
        * Only exists in emulators (global view of the network) 
        * BDP: simple emulated links with a fixed bandwidth and min RRT
          * Bandwidth delay product and use it as the CC window 
        * Search around BDP otherwise 
      * Imitation learning algorithm with DAgger 


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