Optimization of SDN Flow Operations in Multi-Failure Restoration Scenarios
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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