IAGA: Interference Aware Genetic Algorithm based VM Allocation Policy for Cloud Systems
Bibliographic record
Abstract
Diversified systems hosted on cloud infrastructure have to work increasingly on physical servers. Cloud applications running on physical machines require diverse resources. The resource requirements of cloud applications are fluctuating based on the resource intensity of the applications. The multi-tenancy of Cloud servers can be achieved based on effective resource utilization. The optimum resource utilization, maximum service level agreement, and minimization of interference are the major objectives to be achieved. Using live Virtual Machine (VM) migration techniques cloud resources can be utilized efficiently. But the migrated VMs can interfere with the ongoing applications on the targeted server which may lead to the service level agreement violation (SLAV) and performance degradation. To resolve this issue, understanding the current state of cloud hosts before the allocation of newly migrated VM is necessary. This paper presents Interference Attentive Genetic Algorithm (IAGA) based VM allocation strategy to achieve the aforementioned objectives. The proposed IAGA policy has outperformed existing policies for quantifiable performance metrics such as energy consumed by cloud systems, count of hosts shut down, average SLAV, and count of VM migrations.
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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 | 0.001 | 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) | 0.001 | 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".