TinyFlow: Breaking elephants down into mice in data center networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it