Towards Promoting Backup-Sharing in Survivable Virtual Network Design
Bibliographic record
Abstract
In a virtualized infrastructure where multiple virtual networks (or tenants) are running atop the same physical network (e.g., a data center network), a single facility node (e.g., a server) failure can bring down multiple virtual machines, disconnecting their corresponding services and leading to millions of dollars in penalty cost. To overcome losses, tenants or virtual networks can be augmented with a dedicated set of backup nodes and links provisioned with enough backup resources to assume any single facility node failure. This approach is commonly referred to as Survivable Virtual Network (SVN) design. The achievable reliability guarantee of the resultant SVN could come at the expense of lowering the substrate network utilization efficiency, and subsequently its admissibility, since the provisioned backup resources are reserved and remain idle until failures occur. Backup-sharing can replace the dedicated survivability scheme to circumvent the inconvenience of idle resources and reduce the footprints of backup resources. Indeed the problem of SVN design with backup-sharing has recurred multiple times in the literature. In most of the existing work, designing an SVN is bounded to a fixed number of backup nodes; further backup-sharing is only explored and optimized during the embedding phase. This renders the existing redesign techniques agnostic to the backup resource sharing in the substrate network, and highly dependent on the efficiency of the adopted mapping approach. In this paper, we diverge from this dogmatic approach, and introduce ProRed, a novel prognostic redesign technique that promotes the backup resource sharing at the virtual network level, prior to the embedding phase. Our numerical results prove that this redesign technique achieves lower-cost mapping solutions and greatly enhances the achievable backup sharing, boosting the overall network's admissibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".