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Improving QoE via Next-Generation Congestion Control


Compira co-founder Michael Schapira's presentation about next generation congestion control for improved Quality of Experience, at the Streaming Video Alliance Meeting. He talks about why TCP is suboptimal, and various newer forms of congestion control such as Google's BBR and PCC.

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Improving QoE via Next-Generation Congestion Control

  1. 1. Lisbon, November 2019N&T WG Improving QoE via Next-Generation Congestion Control Michael Schapira Co-Founder and Chief Scientist, Compira Labs Professor of CS, Hebrew University
  2. 2. 2 Source: [Maoetal., SIGCOMM2017] Bad Qualityof Experience (QoE) in Video Streaming
  3. 3. Why? The last mile network! The provider’s network/CDN/cloud CDN edge nodes, Video caches The “Last Mile” (ISP, cellular, WiFi, …) Excessive delays, Insufficient bandwidth, High jitter, Loss... Video Clients N&T WG Lisbon, November 2019
  4. 4. Does a faster Internet connection at the client help? Not really Source: Wall Street Journal, August 2019 N&T WG Lisbon, November 2019
  5. 5. queue routerlink link So, where’s the problem? Internet congestion control (CC) N&T WG Lisbon, November 2019
  6. 6. TCP's "sawtooth" behavior ACKs Being received, so increase rate Loss, so decrease rate TIME SendingRate N&T WG Lisbon, November 2019 TCP Est. 1988 Notoriously bad performance in many real-world environments (e.g., mobile/cellular, shallow buffers, dynamic networks, …)
  7. 7. What’s the problem with TCP CC? Suboptimal algorithmic framework TCP Cubic dates back to 1998. To be revisited. ‘One size fits all’ approach Not customized to network conditions Not customized to application needs N&T WG Lisbon, November 2019
  8. 8. Non-congestion loss Shallow buffer Self-induced congestion Congestion from other heavy flows Why is TCP CC suboptimal? N&T WG Lisbon, November 2019
  9. 9. Strong Assumptions » Poor Performance Underlying cause Best response N&T WG Lisbon, November 2019 Packet loss Decrease rate a lot Maintain rate Decrease rate slightly Increase rate Self-induced congestion Congestion from other heavy flows Shallow buffer Non-congestion loss
  10. 10. 2 Recent Approaches to Next Generation CC Vs. White BoxBlack Box Performance-oriented Congestion Control (PCC) Bottleneck-Bandwidth-and- RTT (BBR) N&T WG Lisbon, November 2019
  11. 11. Deployability Both PCC and BBR: Require sender-side changes only No changes to the application (video streamer) needed No changes to the receiver (video client) needed Implemented as an open source Linux kernel module and in QUIC. N&T WG Lisbon, November 2019
  12. 12. BBR from Google N&T WG Lisbon, November 2019 N. Cardwell, Y. Cheng, C.S. Gunn, S.H. Yeganeh, and V. Jacobson. BBR: Congestion- Based Congestion Control, Communications of the ACM, 60(2), February 2017.
  13. 13. Model the network pipe as a single link Seek the optimal operating point N&T WG Lisbon, November 2019 BBR from Google
  14. 14. Track (your fair share of) bottleneck link’s bandwidth. sending rate network queues N&T WG Lisbon, November 2019 BBR from Google
  15. 15. BBR vs. TCP (Cubic) Better throughput Lower packet delays Higher resilience to loss Source: Google N&TWG Lisbon, November2019
  16. 16. But, does BBR’s model capture the last mile? Highly dynamic. Traffic flows enter and leave Short-lived flows. Traffic bursts. Different transport-layer protocols co-exist Routing changes User mobility (handover between base-stations) … N&T WG Lisbon, November 2019
  17. 17. Performance-oriented Congestion Control (PCC) From Hebrew U and UIUC [Dong et. al, NSDI 15+18] N&T WG Lisbon, November 2019
  18. 18. PCC’s utility framework Sending rate r Throughput Loss rate Latency Utility f(tpt, loss, etc.) Internet SACKs N&T WG Lisbon, November 2019
  19. 19. PCC rate control r1 r u1 u2r2 Online rate adaptation algorithm move to Internet N&T WG Lisbon, November 2019 Rates chosen to optimize empirically-observed performance Leverages machinery from online learning theory
  20. 20. PCC tracks the optimal sending rate almost perfectly! Improved Responsiveness Source: [Dong et al., NSDI 2018] Experiment config: BW (10-100Mbps), RTT (10-100ms) and Loss Rate (0-1%) change every 5 seconds N&T WG Lisbon, November 2019 PCC A PCC B
  21. 21. N&T WG Lisbon, November 2019 Demo
  22. 22. Also, PCC’s utility framework supports customization … to different network conditions … to different applications’ needs N&T WG Lisbon, November 2019
  23. 23. A vision for next-generation video streaming The provider’s network/CDN/cloud CDN edge nodes, Video caches Video Clients central application stat collection configuration online sending-rate optimization • Global visibility into QoE • ML-driven big data analytics • Longer-term optimization and customization N&T WG Lisbon, November 2019
  24. 24. Conclusion and Next Steps Next-generation congestion control is crucial for improving QoE in video streaming Irrespective of the transport-layer protocol (TCP, QUIC, etc.) Recent advances suggest promising directions Next steps: Incorporating application-layer QoE statistics into the transport layer POC for NG congestion control at the SVA? N&T WG Lisbon, November 2019

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