EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
Bibliographic record
Abstract
Ant Colony Optimization (ACO) is an intelligent algorithm ensuring optimal resource management in cloud environments. This paper proposes an enhanced version of the ACO algorithm called Enhanced Multiple Ant Colony Optimization for Adaptive Resource Management (EM-ACO-ARM). Our approach uses multiple ant colonies undergoing several iterations of optimizations to find the optimal Virtual Machine (VM) and adapt to the convergence uncertain-ties, unlike a single ant colony in the existing ACO, which can hinder Quality of Service (QoS)-based performance parameters. We conducted experiments in a cloud-simulated environment to evaluate EM-ACO-ARM in two phases. In the first phase, we computed real-time Montage tasks using the existing ACO algorithm on VMs across ten scenarios. To ensure an unbiased comparison, the same cloud configuration was maintained in the second phase, and the same tasks were computed using the proposed EM-ACO-ARM algorithm in all ten scenarios. The experimental results demonstrate that EM-ACO-ARM improves Execution Cost and Execution Time, leading to a 14.73% increase in Resource Utilization. This ultimately improves the management of cloud resources. Additionally, a stability evaluation was conducted using regression models, and it outputted EM-ACO-ARM to provide more stability than the existing ACO algorithm. The cloud can provide better QoS with the proposed EM-ACO-ARM algorithm while abiding by Service Level Agreements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".