Service Commitment Strategies in Allocating Services to Applications
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the problem of commitment in allocating services to different applications and business processes is introduced. We model the problem based on the assumption that the services' instances are not put on hold throughout the lifetime of an application or a business process. Our objective is to find an optimal policy for committing services' instances to different applications to maximize the utilization of available instances of services. We formulate this problem and propose a Markov decision process approach for it. We present the optimal solution for a sample case with two types of services and two classes of application, and compare the performance of this optimal policy with a system with a full commitment policy as well as a No commitment policy system. The comparison results show that the policy obtained outperforms the other two policies. We also evaluate the performance of a system considering beta distribution for the service execution time, and we illustrate the effectiveness of applying the obtained policy on this system as well.
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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 | 0.001 | 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