Optimization of Cloud Service Composition for Data-intensive Applications via E-CARGO
Bibliographic record
Abstract
With the growing cloud services (CSs) rented by an organization, it has become a challenging problem to optimize the CS composition for data-intensive applications (DiAs) from the user side, with consideration of improving the resource utilization of rented CSs. This paper proposes a resource utilization-aware approach to optimizing the CS composition for DiAs (CSCD). From the perspective of role-based collaboration, this approach utilizes the environments - classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD problem. The qualification of a CS for one task is assessed and the compatibility between new tasks and the running task is identified. A solution using IBM ILOG CPLEX package is put forward to optimize the CSCD problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing the resource utilization-aware CSCD problem from the user side.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".