An Optimum Decision Making Framework for Allocating Spot Instances to Execute Batch Workload in Cloud
Bibliographic record
Abstract
Among all available cloud-based solutions, spot instances are ideal for batch job execution with flexible completion times and failure tolerance. The combination of spot instance sizes and prices provides flexibility to execute jobs satisfactorily with regards to their computational requirements. However, the demand for each type of batch instance fluctuates over time and it raises some challenges to provide adequate instances to match the workload demand. This makes it necessary to have a mechanism to dynamically adjust the number of instances and assign them to the incoming jobs.In this paper, we propose an optimization-based solution to allocate spot resources for executing arriving batch jobs in the cloud platforms over time. This method manages the trade-off between the cost of renting spot instances and the user-centric quality of service in terms of job waiting time. Given a set of jobs and spot instances of various types, our algorithm selects the best combination of instances in each analysis window. In addition, the method takes into account the job arrival prediction and characteristics of input jobs to adapt the algorithm.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".