Energy-Aware Inter-Data Center VM Migration Over Elastic Optical Networks
Bibliographic record
Abstract
The rapid growth of data processing demands in large-scale data centers (DCs) has led to increased brown energy (BE) consumption, which has negative environmental impacts. Since most DCs are now powered by both BE and renewable energy (RE), migrating workloads from DCs with insufficient RE to DCs with sufficient RE can decrease the total BE consumption in the network. However, selecting a destination DC is challenging due to the uncertainty of the network and the additional cost associated with using network devices for the migration. This paper proposes to optimize the DC selection and the efficient virtual machine transfer between DCs, minimizing the costs of BE consumption, optical network devices, and migration. Specifically, we formulate the DC selection as a multi-armed bandit problem and estimate the lowest migration cost at each round using the lower confidence bound. We adopt the optical grooming technique to reduce the cost of optical devices used during the migration. We compare our algorithm with the KUBE and -Greedy algorithms on the NSFNET and show that it reduces the total cost by 4.6% and 12.8%, respectively, while having lower regret. We demonstrated the effectiveness of optical grooming by achieving a 12 % reduction in network costs.
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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 | 0.003 |
| 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".