Credence: Augmenting Datacenter Switch Buffer Sharing with ML Predictions

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" summary

CREDENCE augments commodity drop-tail buffers with ML predictions to emulate push-out behavior, using per-queue thresholds and an “arithmetic push-out” trick (add/subtract sizes) plus a safeguard against false positives. With perfect predictions it matches near-optimal LQD and gracefully degrades to Complete Sharing under heavy prediction error. Packet-level ns-3 eval on a 256-server topology with a random forest predictor shows about 1.5x throughput and up to 95% flow completion time reduction versus prior drop-tail schemes.

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" chapters (12)

1. 0:00Credence introduction
2. 1:00Game analogy for buffer sharing
3. 2:49Buffer sharing problem
4. 3:55Online perspective and competitive ratio
5. 5:20Drop tail vs push out
6. 6:23Limitations of drop tail
7. 7:48Predictions to improve drop tail
8. 9:15Goals: robustness and smoothness
9. 9:59Credence algorithm design
10. 12:12Evaluation and results
11. 14:23Future research directions
12. 15:25Summary and limitations

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