Online Non-preemptive Multi-Resource Scheduling for Weighted Completion Time on Multiple Machines
Bibliographic record
Abstract
Jobs in computing environments have diverse and heterogeneous resource requirements. This paper presents a study of online, non-preemptive scheduling algorithms for multiple identical machines under the average weighted completion time objective. The key challenge addressed is resource allocation to jobs with non-uniform demands across multiple resource types, such as CPU, memory, and storage. We propose an online algorithm, termed Multi-Resource Interval Scheduling (MRIS) that achieves a competitive ratio of 8R(1 + ϵ) for the average weighted completion time, where R is the number of resource types. To the best of the authors’ knowledge, this is the first theoretical competitive analysis under the considered system. We further show that the well-known priority queue algorithms can have arbitrarily bad competitive ratios in this setting. In numerical experiments using production workload traces from Microsoft Azure, the proposed algorithm is shown to significantly outperform priority queue algorithms and other state-of-the-art schedulers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".