A User Relinquishment-based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers
Bibliographic record
Abstract
In a cloud federation, by using the pay-as-you-go billing model users can relinquish their services at any point in time and pay accordingly.Therefore, this thesis aims to study the resource assignment problem in the situation where the user relinquishment impacts the net profit of a cloud service provider.As a solution, our study 1) proposes a tool to calculate the net profit which includes income, electricity expenses, and relinquishment loss; 2) compares different ways to predict the user behavior and deduce a better prediction technique based on linear regression; and 3) proposes a relinquishment-aware resource optimization model to estimate the amount of resources based upon the predicted user behavior.Simulations were performed with the CloudSim framework.The results show that instead of blindly assigning resources to users, a cloud service provider with a finite resource pool can gain more by estimating the resources using better prediction techniques.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".