Improving flow completion time for short flows in datacenter networks
Bibliographic record
Abstract
Today's cloud datacenters host wide variety of applications which generate diverse mix of internal datacenter traffic. In a cloud datacenter environment 90% of the traffic flows, though they constitute only 10% of the data carried around, are short flows with sizes up to a maximum of 1MB. The rest 10% constitute long flows with sizes in the range of 1MB to 1GB. Throughput matters for long flows whereas short flows are latency sensitive. Datacenter Transmission Control Protocol (DCTCP) is a transport layer protocol that is widely deployed at datacenters nowadays. DCTCP aims to reduce the latency for short flows by keeping the queue occupancy at the datacenter switches under control while ensuring throughput requirements are met for long flows. But, DCTCP congestion control algorithm treats short flows and long flows equally. We demonstrate that treating them differently, by reducing the congestion window for short flows at a lower rate compared to long flows at the onset of congestion, we could improve the flow completion time for short flows by up to 25%, thereby reducing their latency up to 25%. We have implemented a modified version of DCTCP for cloud datacenters, based on the DCTCP patch available for Linux, which achieves better flow completion time for short flows while ensuring that throughput of long flows are not affected.
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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 | 0.003 | 0.007 |
| 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".