Mitigating the negative impact of preemption on heterogeneous MapReduce workloads
Bibliographic record
Abstract
Modern production clusters are often shared by multiple types of jobs with different priorities in order to improve resource utilization. Preemption is a common technique employed by MapReduce schedulers to avoid delaying production jobs while allowing the cluster to be shared by other non-production jobs. In addition, it also prevents a large job from occupying too many resources and starving others. Recent literature shows that jobs in production MapReduce clusters have a mixture of lengths and sizes spanning many orders of magnitude. In this type of environments, the current preemption policy used by MapReduce schedulers can significantly delay the completion time of long running tasks, resulting in waste of resources. This paper firstly discusses the heterogeneous nature of MapReduce jobs and their arrival rates in several production clusters. Secondly, we characterize the situations where the current preemption policy causes significant preemption penalty. We then propose a simple mechanism that works in conjunction with existing job schedulers to address this problem. Finally, we evaluate our solution under various types of workloads in Amazon EC2. Experiments show our method can improve system normalized performance by 15% during busy periods by effectively avoiding unnecessary preemption while preserving fairness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".