Probabilistic Virtual Link Embedding Under Demand Uncertainty
Bibliographic record
Abstract
This paper considers the problem of mapping virtual links to physical network paths, referred to as Virtual Link Embedding (VLE), under the condition that bandwidth demands of virtual links are uncertain. To realize virtual links with predictable performance, the mapping is required to guarantee a bound on the congestion probability of the physical paths that embed the virtual links. To this end, we consider a general uncertainty model in which bandwidth demands of virtual links are expressed by random variables for which only the mean and variance (or a range) are known. We formulate the VLE problem as a nonlinear optimization program and design an algorithm called Equal Partition VLE (epVLE) to solve the problem by employing an approximate formulation that results in a second-order cone program (SOCP) that can be solved efficiently even for large networks. We then provide simulation results as well as model-driven and trace-driven experimental results from an SDN testbed to show the utility and efficiency of the epVLE algorithm in various network scenarios. We apply epVLE to commonly studied small networks as well as randomly generated large networks. Our results show that epVLE is able to satisfy the required link congestion constraint, and that it produces results that are very close to those obtained from the exact optimization model.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".