Multi-failure restoration with minimal flow operations in software defined networks
Bibliographic record
Abstract
We consider dynamic flow restoration in multi-failure scenarios in OpenFlow-based Software-Defined Networks (SDNs). Flexibility of network configuration in these networks makes it possible to dynamically restore flows in case of multilink failures. To re-route the failed flows, network devices such as switches carry out flow operations, i.e., add new flow-entries to the flow-tables. In disaster scenarios where thousands of flows must be restored in a short time, the time required to perform such operations is significant and must be minimized to maintain a carrier-grade network. Shortest-path based techniques do not take into account the number of flow operations (namely, operation cost) and therefore are inefficient for disaster scenarios. We incorporate the operation cost into the flow restoration problem, and formulate the problem of finding a path 1) with the lowest path cost with capped operation cost, 2) with the lowest possible operation cost, and 3) with the minimum operation cost amongst all the paths with a Dijkstra-like path cost. We propose optimal algorithms with Dijkstra-like complexity for the second and third problems. The simulation results with European Reference Network (ERnet) show that our proposed methods on average can reduce the number of required flow operations up to 15% while the path cost rises less than 3%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".