Simultaneous Cost and QoS Optimization for Cloud Resource Allocation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing is a new era of computing that offers resources and services for Web applications. Selection of optimal cloud resources is the main goal in cloud resource allocation. Sometimes, customers pay more than required since cloud providers' pricing strategy is designed for the interest of the providers. Nonetheless, cloud customers are interested in selecting cloud resources to meet their quality of service (QoS) requirements. Thus, for the interest of both providers and customers, it is vital to balance the two conflicting objectives of deployment cost and QoS performance. In this paper, we present a cost-effective and runtime friendly algorithm that minimizes the deployment cost while meeting the QoS performance requirements. In other words, the algorithm offers an optimal choice, from customers' point of view, for deploying a Web application in cloud environment. The multi-objective optimization algorithm minimizes cost and maximizes QoS performance simultaneously. The proposed algorithm is verified by a series of experiments on different workload scenarios deployed in two distinct cloud providers. The results show that the proposed algorithm finds the optimal combination of cloud resources that provides a balanced trade-off between deployment cost and QoS performance in relatively low runtime.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it