Surviving link failures in multicast VN embedded applications
Bibliographic record
Abstract
Virtual network embedding (VNE) is defined as the allocation of network resources to multiple virtual networks (VNs) and is recognized to be a challenging task to perform efficiently. Virtual network survivability is a new term that describes the measures taken to provide a failure-proof VN against physical link and/or node failure. Indeed, a single link or node failure in a substrate network can bring down multiple hosted VNs, i.e., the ones that utilize that failed link or node. As such, virtual network survivability becomes an essential part of VNE. While much work has been dedicated to studying the impact of a variety of failure cases in a VN, little attention has been directed towards studying the link failure impact on multicast virtual network (MVN) applications, which principally restrict end-to-end delay and delay variation measures. In fact, most of the introduced survivability schemes adopt protection techniques by reserving backup resources prior to embedding, which inevitably leads to under-utilization of the network resources. In this paper, we first investigate the impact of physical link failure on MVNs. Then, we introduce a novel recovery approach to restore MVNs while considering their end-delay and delay variation requirements. Simulation experiments prove that our recovery technique achieves good restoration ratio in considerably fast execution time and low link mapping cost with little impact on the admittance ratio.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".