Scheduling Job Streams on Uniprocessors with Cold Start Delays
Bibliographic record
Abstract
We consider uniprocessor job scheduling where jobs have deadlines and each job belongs to a job family. Each job family has an associated setup time or cold start delay, and when a job is scheduled, if the predecessor job does not belong to the same job family then this setup time needs to be included. We examine the scheduling problem when the objective is to minimize the number of tardy jobs, and the challenge of including the setup time for switching between job families results in poor performance of well-known policies such as earliest deadline first (EDF). We propose a near-optimal online scheduling policy for jobs with deadlines, on uniprocessor platforms. This problem arises in a variety of contexts including serverless computing and MLaaS (machine learning as a service) where a job request may need a suitable resource container to be provisioned if an earlier request was not of the same type. The general offline problem of job scheduling with job families and setup costs has previously been studied and shown to be NP-Hard. In an effort to improve our understanding of the online problem with the objective of maximizing the number of jobs that meet their deadlines, we focus on the case where all jobs have the same execution time. We show that even this special case is NP-Hard in the offline setting. The policy we propose, which requires job buffering, is nearly 1-competitive when each job has a reasonably large slack. We also propose a heuristic that performs well in many situations despite a weak competitive ratio.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".