Optical inter-data-center network design under resilience requirements and dynamic electricity pricing
Bibliographic record
Abstract
Workloads submitted to the Cloud are handled within the data centers in a distributed manner. Reducing the operational expenses is a fundamental concern for the operators which can be addressed by efficient design of the inter-data-center network. Optical networks have been reported to be the best transport media to accommodate the high capacity inter-data-center traffic. Furthermore, employment of elastic optical networks can further improve bandwidth utilization over the backbone. In this paper, we present our solution to minimize the operational expenses of the operators through resilient design of an inter-data-center network in the presence of dynamic electricity pricing, namely Time-of-Use tariffs. The proposed solution periodically virtualizes the inter-data-center network topology based on previously forecasted demand intensities with the joint objective of minimum outage probability and minimized electric bill for the data center and network operators. Through simulations, we compare our solution to a naïve resilient design solution and another design approach aiming at minimum energy consumption. We discuss the benefits of our proposal in terms of both objectives and provide insights towards challenges and opportunities in this field.
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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 | 0.000 | 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".