Minimizing Deployment Cost of Cloud-Based Web Application with Guaranteed QoS
Bibliographic record
Abstract
Cloud computing provides a reliable and cost- effective setting for deploying large-scale web applications. However, choosing and configuring an appropriate cloud Infrastructure-as-a-Service (IaaS), e.g., the appropriate database and computing instances and acceptable service rates, is a daunting task. The task is also challenging when trying to optimize the IaaS for conflicting objectives such as performance and cost. Furthermore, due to lack of understanding of the pricing model and the cloud IaaS, a cloud consumer may pay more than necessary or may not fully utilize the purchased resources. For this reason, we propose an algorithm that suggests the most cost-effective configuration meeting the QoS requirements and budget constraint. In contrast to existing cost optimization proposals, our proposed algorithm maps the minimum requirements of the to- be-deployed web application to deployment costs according to the price model set by cloud providers. The algorithm also considers QoS requirements for different resource types in the cloud, namely, database servers, computing servers, storage, and service rate. The proposed algorithm is evaluated by a series of experiments on a web application with seven different workload scenarios. The experimental results show the effectiveness of the proposed algorithm in achieving a solution with the minimum deployment cost for each scenario while satisfying all customer's requirements.
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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 | 0.003 |
| 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 | 0.000 |
| 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".