Improving QoS for VM Allocation in Multi-Cloud Environment
Bibliographic record
Abstract
Managing resources in a cloud environment is a challenging problem, especially in multi-cloud settings due to the size and complexity of the cloud environment and the variable demands of the customers. Before assigning a customer any resources from a Cloud Service Provider (CSP), it is crucial to evaluate the CSP’s pricing, availability, and reliability to ensure the cloud environment maintains its Quality of Service (QoS) and meets customer expectations. Our research addresses the resource allocation problem in a multi-cloud environment while maximizing the reliability and minimizing the price and latency to improve QoS. We formulate the resource allocation problem as a multi-objective optimization model and propose a heuristic to solve the problem. The proposed model provides flexibility by adjusting the weight parameters based on the customers’ requirements. We compare the performance of the proposed heuristic with the baseline-optimized solution for various simulation settings and scenarios. The simulation results demonstrate that the execution time of the proposed heuristic to allocate resources requires significantly less time compared to the optimized solution while achieving near-optimal solutions. Additionally, achieving the consistent performance trend of the proposed model for varying simulation settings and scenarios reveals the applicability of the model for resource allocation in a multi-cloud environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| 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".