TinyFlow: Breaking elephants down into mice in data center networks
Bibliographic record
Abstract
Current multipath routing solution in data centers relies on ECMP to distribute traffic among all equal-cost paths. It is well known that ECMP suffers from two deficiencies. ECMP does not differentiate between elephant and mice flows, creates head-of-line blocking for mice flows in the egress port buffer, and results in long tail latency. Further it does not fully utilize available bandwidth due to hash collision among elephant flows. We propose TinyFlow, a simple yet effective approach that remedies both problems. TinyFlow changes the traffic characteristics of data center networks to be amenable to ECMP by breaking elephants into mice. In a network with a large number of mice flows only, ECMP naturally balances load and performance is improved. We conduct NS-3 simulations and show that TinyFlow provides 20%-40% speedup in both mean and 99-th percentile FCT for mice, and about 40% throughput improvement for elephants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".