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Towards provably performant congestion control

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Framework creates a belief set that exhaustively maps observed send/ACK time series to possible bottleneck bandwidth, buffer, and delay states, and uses SMT-based synthesis to generate short, implementable rate-decision programs that come with formal guarantees and proven impossibility results. Results show a concrete loss vs convergence trade-off: tolerating more loss lets algorithms converge in time logarithmic in bandwidth (as BBR-like probes do) versus linear for conservative AIMD, and synthesized CCAs are implemented and validate controlled probing and draining behavior.

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  1. 0:00Introduction
  2. 1:07Traditional congestion control limits
  3. 4:41Belief set representation
  4. 9:20Impossibility results with belief sets
  5. 13:40Synthesizing congestion control algorithms
  6. 14:23Trade-offs in objectives
  7. 16:49Summary
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