Multi-resource fair sharing for datacenter jobs with placement constraints
Bibliographic record
Abstract
Providing quality-of-service guarantees by means of fair sharing has never been more challenging in datacenters. Due to the heterogeneity of machine configurations, datacenter jobs frequently specify placement constraints, restricting them to run on a particular class of machines meeting specific hardware/software requirements. In addition, jobs have diverse demands across multiple resource types, and may saturate any of the CPU, memory, or storage resources. Despite the rich body of recent work on datacenter scheduling, it remains unclear how multi-resource fair sharing is defined and achieved for jobs with placement constraints. In this paper, we propose a new sharing policy called Task Share Fairness (TSF). With TSF, jobs are better off sharing the datacenter, and are better off reporting demands and constraints truthfully. We have prototyped TSF on Apache Mesos and confirmed its service guarantees in a 50-node EC2 cluster. Trace-driven simulations have further revealed that TSF speeds up 60% of tasks over existing fair 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".