Multi-objective ACO virtual machine placement in cloud computing environments
Bibliographic record
Abstract
Cloud computing systems provide services to users based on a pay-as-you-go model. The more services that data centers deliver to users, the more those centers need to be prepared. However, data centers consume huge amounts of energy from the environment. In order to improve data-center efficiency, resource consolidation using virtualization technology is becoming important for the reduction of the environmental impact caused by the data centers. One of the important keys in resource consolidation is the mapping of virtual machines to suitable physical machines, a procedure called virtual machine placement. The present paper focuses on this problem of virtual machine placement and proposes a multi-objective optimization approach to minimize both power consumption and resource wastage and to minimize energy communication cost between network elements within a data center. An Ant Colony Optimization (ACO) algorithm is proposed to obtain a Pareto set for a multi-objective problem. The proposed algorithms are tested using Cloudsim tools. The performances of these algorithms are compared with three well-known single-objective approaches and a multi-objective Genetic Algorithm (GA). The results demonstrate that the proposed algorithms can seek and find solutions that exhibit balance between different objectives. However, ACO is able to And better solutions than GA in terms of our objectives.
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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 | 0.001 | 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 | 0.001 |
| 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".