Optimization of SDN Flow Operations in Multi-Failure Restoration Scenarios
Bibliographic record
Abstract
Flexible network configuration in software-defined networks makes it possible to dynamically restore flows. To this end, network devices carry out flow operations (i.e., adding or removing flow-entries to/from the flow-tables) to re-route the disrupted flows. Current flow restoration techniques do not consider the number of operations, and hence, are inefficient in disaster scenarios. We aim to minimize the number of operations in such cases and formulate integer programs to find a path: 1) with the lowest path cost requiring up to a given number of operations; 2) requiring the fewest possible operations; and 3) with a Dijkstra-like path cost requiring minimum operations. We study the tradeoff between path cost and the number of operations and prove that the second and third problems are polynomial-time solvable. We propose optimal/suboptimal algorithms with Dijkstra-like complexity that find nearly-optimal solutions. The simulation results show that our methods reduce the number of operations up to 50%, and the best performance is achieved when the number of failed links is small.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".