Cupid: Congestion-free consistent data plane update in software defined networks
Bibliographic record
Abstract
With the popular applications of SDN in load balancing and failure recovery, the controller schedules affected flows to redundant paths to avoid network congestions and failures by updating flow tables in data plane. However, inconsistent flow table updating may lead to transient incorrect network behaviors or undesired performance degradation. Therefore, the consistency imposes dependencies among updates, so that the order of updates must be carefully considered to keep the consistency. To update flow tables consistently and efficiently, in this paper, we propose an update ordering approach — Cupid. To avoid high overhead in update ordering, we divide the global dependencies among updates into local restrictions by: 1) partitioning a new routing path into several independent segments, 2) identifying critical nodes controlling traffic shifting between the old path and new path, and 3) constructing a dependency graph among critical nodes for potential congested links. We then design a heuristic algorithm to resolve the dependency graph. To save the flow table space, a switch keeps only one flow entry with multiple ports for a flow during updating. Our simulation shows that Cupid schedules updates at least 2 times faster and has less throughput losses than the state-of-the-art approaches in both fat-tree and mesh networks.
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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 | 0.002 | 0.005 |
| 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 | 0.003 | 0.002 |
| 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".