Live Placement of Interdependent Virtual Machines to Optimize Cloud Service Profits and Penalties on SLAs
Bibliographic record
Abstract
This paper aims to optimize cloud services' net profits and penalties with live placement of interdependent virtual machines (VMs). This optimization is a complex task as it is difficult to achieve a successful compromise between penalties and net profits on service level contracts. This paper studies this optimization problem to minimize services' penalties and maximizing net profits while achieving live migrations of interdependent VMs. This VM's live placement optimization problem is a NP-hard problem with exponential running time. A mathematical model was designed and approximations were conducted with an efficient PCH/PCH' heuristic. This Mixed Integer Non-Linear programming (MNLP) formulation and heuristic for cloud services was tested where the overall services' penalty needs to be minimized, overall net profits have to be maximized, and where efficient live migrations of VMs is a concern. Simulation results show how cloud providers may live place VMs. Finally, our results show that a PCH/PCH' heuristic: (i) finds better solutions than the existing machines' configuration of Google traces; (ii) is suitable for large-sized instances of cloud services; (iii) performs better than FF, FFD, and CPLEX in terms of overall penalties and net profits; and (iv) runs in less than six minutes over the last day's data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".