Optimized Availability-Aware Component Scheduler for Applications in Container-Based Cloud
Bibliographic record
Abstract
Cloud Computing is a popular platform for providing online computing services to the tenants. With migrating large number of the applications to the cloud, application availability is raised as a concern issue. Availability is a non-functional requirement that refers to the percentage of time the application service is available for the use. Many factors can impact the application availability especially in the cloud environment. In this paper, we show the impact of scheduling (placement) of the application's components inside the Data Center of the cloud on the application availability. We propose an optimized Availability- Aware strategy to schedule the components with the goal to maximize the availability of the application while satisfying several constraints. We map the scheduling problem to Integer Linear Programming (ILP) optimization model. We compare the Availability-Aware strategy with other scheduling strategies. The results show that the Availability-Aware strategy achieves higher application availability compared to the other strategies.
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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 | 0.001 | 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 | 0.000 |
| 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 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".