Availability-Aware Container Scheduler for Application Services in Cloud
Bibliographic record
Abstract
Cloud is a popular paradigm for providing online computing services to the end users. Recently many of the cloud service providers use containers instead of Virtual Machines (VMs) to host the applications. Using containers raises the application service availability concerns. Availability is a non-functional requirement that refers to the percentage of time the service is available for the end user. Scheduling containers (host application) on physical/virtual hosts has direct impact on the availability of the application service that is provided to the end user. According to the best of our knowledge, the existing container scheduling solutions do not directly address the availability of the application service. In this article we propose a new Availability-Aware container scheduling strategy that aims to increase the availability level of the application service in the cloud container-based platform. The strategy selects VMs and hosts that have higher availability values within constraints to schedule the containers in efficient way. We compare the proposed strategy with other container scheduling strategies that are used by Docker container platform. The results shown that the Availability-Aware strategy achieves higher service availability levels, and acceptable physical host CPU utilization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".