Learning in situ: a randomized experiment in video streaming

https://www.usenix.org/system/files/nsdi20-paper-yan.pdf

Presentation

• Video streaming dominates internet traffic

• Adaptive bitrate (ABR) top optimize users' quality of experience (QoE)

  • Decides the quality level of each video chunck to send

  • Primary goals: higher video quality, fewer stalls

  • Prior work: BBA, MPC, CS2P, Pensieve, Oboe

• What does it take to create a learned ABR algorithm that robustly performs well over the wild internet?

  • Confidence intervals in video streaming are bigger than expected

    • Puffer: a live streaming platform running a randomized experiment

    • Randomized experiments (one of the ABR scheme being tested)

    • 
    • Existing ABR algorithms found benefits like 10%-20% based on experiments lasting hours or days between a few network nodes

    • Need 2 years of data per scheme are needed to measure 20% precision

    • 

      • Want higher video quality: y axis

      • And fewer stalls: x axis

      • Better QoE: up and to the right

      • Most schemes are statically indistinguishable (noise)

    • Reason: Internet is way more noisy and heavy-tailed than we thought

      • Only 4% of the 637,189 streams had any stalls

      • Distributions of throughputs and watch times are highly skewed

  • A simple (buffer-based) ABR algorithm performs better than expected

    • BBA [SIGCOMM '14]: simple buffer-based ABR algorithm

      • Buffer: allows a streamed video or media file to be loaded while the user is watching or listening to it. Pre-download and store video in a temporary cache before playback begins on whatever device you are using

    • 
    • Research baseline

    • Other work: MPC-HM [SIGCOMM '15]

      • Predicts throughput using the harmonic mean (HM) of past throughputs

        • assumes throughput can be modeled with HM

        • assumes transmission time = predicted throughput x chunk size?

      • But the observed throughput actually vary with chunk size, due to congestion control and varying bandwidth

    • Other work: Pensieve [SIGCOMM '17]

      • Reinforcement learning

        • Requires network simulators as training environments

        • Assumes training in simulation generalizes to wild Internet?

        • 
    • Comparison

      • 
      • No algorithm can perform better than BBA in both aspects

    • Algorithms that make fewer assumptions are perhaps more general

  • Our way of outperforming existing schemes is learning in situ (i.e. in place on the actual deployment environment)

    • Fugu uses classical model predictive control

    • Fugu replaces the throughput predictor in MPC-HM with a transmission time predictor

      • NN-based: predict how long it takes for a client to receive a given chunk. "How long would each chunk take?"

        • Input:

          • Size and transmission times of past chunks

          • Size of a chunk to be transmitted (not a throughput predictor)

          • Low-level TCP statistics (min RTT, RTT, CWND, packets in flight, delivery rate)

        • Output:

          • probability distribution over transmission time (not a point estimate)

            • Useful for maximize expected QoE

      • 
      • Training: supervised learning in situ (in place) on real data from deployment environment

        • Chunk-by-chunk series of each individual video stream

        • Chunk i: size, timestamp sent, timestamp acknowledged, TCP statistics right before sending

      • Learning in situ does not replay throughput traces or require network simulators

        • We don't know how to faithfully simulate the Internet

• Refine: pensive (with puffer traces)

• Learning in situ: directly simulate from the real environment