Elephant Flow Detection and Load-Balanced Routing with Efficient Sampling and Classification
Bibliographic record
Abstract
SDN (Software defined networking) provides effective technical methods for optimal resource management. However, there are resource conflicts frequent and serious in current related schemes because they mix elephant and mice flows on shared transmission paths. So, controllers in SDN have to be smart enough to detect elephant flows with low cost and then reroute elephant and mice flows in a feature-aware way. However, existing elephant flow detection schemes suffer from high bandwidth consumption and long detection time; and little literature considers mice-flow scheduling. In this paper, we propose an Efficient Sampling and Classification Approach (ESCA). Our ESCA significantly reduces sampling overhead through estimating the arrival interval of elephant flows and filtering out redundant samples, and efficiently classifies samples with a new supervised classification algorithm based on correlations among data flows. Then, based on our low-cost ESCA, we propose a novel load-balanced routing approach LBRouting that sets up paths for elephant and mice flows with different mechanisms. The theoretical analysis proofs our ESCA outperforms related schemes. Extensive experiment results further demonstrate that our ESCA can provide accurate detection with less sampled packets and shorter detection time; and our routing approach LBRouting significantly outperforms related proposals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".