Optimal Cloud Broker Method for Cloud Selection in Mobile Inter-cloud Computing
Bibliographic record
Abstract
In the next generation of Cloud computing systems (so-called Inter-Cloud), it is expected that multiple Cloud Service Providers (CSPs) will collaborate or cooperate together to form the so-called Cloud Market, with the goal to advertise their services and associate prices to their end users. In this environment, mobile users will be able to better negotiate and select the CSP that better suits their budgetary and technical needs in a more flexible manner. However, despite the benefit of having multiple CSPs to choose from, mobile users will have to cope with several issues, for instance, that of selecting a suitable CSP to handle the incoming service request from the users. To address this problem, this paper proposes an optimal Cloud Broker. The role played by the optimal Cloud Broker is to provide the best possible match for mobile users and CSPs. The Cloud Broker design is formulated by using the framework of Semi-Markov Decision Process (SMDP). Considering the average cost criterion as the optimality criterion, the Value Iteration Algorithm is used to find the optimal Broker policy. Numerical results are presented, demonstrating the effectiveness of our proposed Broker design, in a scenario where a wireless service provider has its own Cloudlet and a Service level Agreement (SLA) has been established with a public CSP to handle the overload occurrences.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".