Resilient optical inter-data-center network design
Bibliographic record
Abstract
Optical networks have been shown to be the most viable solution for inter-data-center network implementation. Furthermore, elastic optical networks incorporating virtualization technology can significantly introduce agile delivery of cloud services. In this paper, we present resilient design of an inter-data-center network over an elastic optical network backbone aiming at minimum outage probability for the demands submitted to the Cloud and handled in one or more data centers in the network where a data center can have any of the following four availability values; 99.67%, 99.74%, 99.98% and 99.995%. Moreover, employment of optical network as the transport medium introduces the high availability advantage of the optical network components over the lightpaths/light-trees towards data centers. We illustrate the advantages of manycast-based provisioning of the upstream data center requests over anycast-based provisioning and minimum-cost provisioning in terms of outage probability. Furthermore, we show the impact of resilient design of the inter-data-center network on the path delay performance of upstream data center demand.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".