Congestion-Constrained Virtual Link Embedding with Uncertain Demands
Bibliographic record
Abstract
Network virtualization enables multiple virtual net-works to co-exist on the same physical network. Each virtual network requires specific amounts of physical network resources such as node processing and link bandwidth. The problem of mapping virtual resource requirements to physical resources is extensively studied in the literature under the assumption that resource demands of virtual networks are known deterministically. In real deployments though, resource demands include significant uncertainty and fluctuate over time. This paper considers the problem of mapping virtual links to physical network paths subject to a constraint on each virtual link congestion probability under the assumption that bandwidth demands of virtual links are uncertain. A general uncertainty model is considered, where bandwidth demands are described by random variables for which only the mean and variance (or a range) are known. We formulate the problem as a nonlinear optimization problem, which is shown to be non-convex. Consequently, we develop 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 as well as Mininet experimental results to show the utility and efficiency of our exact and approximate models in various network scenarios. We apply our models to commonly studied USA and EON networks as well as randomly generated large networks. Our results show that both models are able to satisfy the link congestion constraint, and that the approximate model is very close to the exact 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".