An End-to-End QoS Mapping Approach for Cloud Service Selection
Why this work is in the frame
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Bibliographic record
Abstract
In order to select and rank the best services in a cloud computing environment, the end-to-end quality of service (QoS) values of cloud services have to be computed. For a new SaaS provider, the deployment of its software application in the cloud is a challenging job. It has to find a hosting service (IaaS) that hosts its service. The primary goal of the SaaS provider is to make its service at the top of the ranked list of cloud services returned to end users through satisfying their QoS requirements. In this paper, we propose a mechanism to map the users' QoS requirements of cloud services to the right QoS specifications of SaaS then map them to best IaaS service that offers the optimal QoS guarantees. Then together SaaS and IaaS services can provide the best service offer to end users. As a result of the mapping, the end-to-end QoS values can be calculated. We propose a set of rules to perform the mapping process. We hierarchically model the QoS specifications of cloud services using the Analytic Hierarchy Process (AHP) method. The AHP based model helps to facilitate the mapping process across the cloud layers, and to rank the candidate cloud services for end users. We use a case study to illustrate and validate our solution approach.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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