Design of energy‐efficient cloud systems via network and resource virtualization
Bibliographic record
Abstract
Summary Data centers play a crucial role in the delivery of cloud services by enabling on‐demand access to the shared resources such as software, platform and infrastructure. Virtual machine (VM) allocation is one of the challenging tasks in data center management since user requirements, typically expressed as service‐level agreements, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors to greenhouse gas emissions of IT services. In this paper, we propose a holistic approach for a large‐scale cloud system where the cloud services are provisioned by several data centers interconnected over the backbone network. Leveraging the possibility to virtualize the backbone topology in order to bypass IP routers, which are major power consumers in the core network, we propose a mixed integer linear programming (MILP) formulation for VM placement that aims at minimizing both power consumption at the virtualized backbone network and resource usage inside data centers. Since the general holistic MILP formulation requires heavy and long‐running computations, we partition the problem into two sub‐problems, namely, intra and inter‐data center VM placement. In addition, for the inter‐data center VM placement, we also propose a heuristic to solve the virtualized backbone topology reconfiguration computation in reasonable time. We thoroughly assessed the performance of our proposed solution, comparing it with another notable MILP proposal in the literature; collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization and fairness for medium size data centers. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".