Mitigating Datacenter Incast Congestion Using RTO Randomization
Bibliographic record
Abstract
TCP incast congestion happens in many-to-one communication workflow patterns that frequently arise in large-scale datacenter applications such as web search, social networks, and cluster-based storage systems. Incast congestion can severely degrade the performance of applications. This paper studies the effectiveness of randomizing the TCP retransmission timeout (RTO) in mitigating the impact of incast. Our design is based on the observation that under incast, retransmitted packets also get synchronized due to the use of similar RTOs by the senders. Using analysis and experimental evaluation, we show that there exists a tradeoff between the randomization interval (from which the RTO values are picked) and the number of senders involved in incast. Motivated by this insight, we propose three algorithms (TDA, MAA, and FSA) for the dynamic adaptation of the randomization interval that rely on (a) successive timeouts, (b) explicit knowledge of the level of multiplexing, and/or (c) the knowledge of flow sizes (i.e., large interval for long flows and a small interval for short flows), respectively. Our results show that these algorithms improve goodput by 1.5x-11x for up to 64 senders and provide greater improvement for larger number of senders. The proposed algorithms can be readily deployed as they do not require any changes in switches or applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".