Time of use (ToU)-awareness with inter-data center workload sharing in the cloud backbone
Bibliographic record
Abstract
Cloud computing is the leading edge concept which combines the advantages of several existing computing concepts for the betterment of the Information and Communication Technology (ICT) business. This new business model aims at moving the services such as software, platform and/or infrastructure to a shared pool of resources which are mainly housed in the data centers. In this paper, we propose a novel virtualization scheme for the cloud network with the objective of provisioning the demands among the data centers in a Time-Of-Use (ToU) pricing-aware manner while ensuring maximum energy savings in the cloud network throughout the day. In addition to the unicast demands between backbone nodes, upstream user demand destined to data centers, and downstream data center demands originating from many data centers, here, we also consider inter-data center traffic in order to enable workload sharing between the data centers. Through numerical results, we show that significant savings in terms of operational expenditures (Opex) can be achieved while demands can be provisioned with less energy consumption in the data centers and network equipments. Furthermore, we show that incorporation of inter-data center workload sharing in ToU-aware provisioning can mitigate the increased propagation delay introduced to the user demands submitted to the cloud.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".