Improving short job latency performance in hybrid job schedulers with dice

Wei Zhou, K. Preston White, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

It is common to find a mixture of both long batch jobs and latency-sensitive short jobs in enterprise data centers. Recently hybrid job schedulers emerge as attractive alternatives of conventional centralized job schedulers. In this paper, we conduct trace-driven experiments to study the job-completion-delay performance of two representative hybrid job schedulers (Hawk and Eagle), and find that short jobs still encounter long latency issues due to fluctuating bursty nature of workloads. To this end, we propose Dice, a general performance optimization framework for hybrid job schedulers, to alleviate the high job-completion-delay problem of short jobs. Dice is composed of two simple yet effective techniques: Elastic Sizing and Opportunistic Preemption. Both Elastic Sizing and Opportunistic Preemption keep track of the task waiting times of short jobs. When the mean task waiting time of short jobs is high, Elastic Sizing dynamically and adaptively increases the short partition size to prioritize short jobs over long jobs. On the other hand, Opportunistic Preemption preempts resources from long tasks running in the general partition on demand, so as to mitigate the "head-of-line" blocking problem of short jobs. We enhance the two schedulers with Dice and evaluate Dice performance improvement in our prototype implementation. Experiment results show that Dice achieves 50.9%, 54.5%, and 43.5% improvement on 50th-percentile, 75th-percentile, and 90th-percentile job completion delays of short jobs in Hawk respectively, as well as 33.2%, 74.1%, and 85.3% improvement on those in Eagle respectively under the Google trace, at low performance costs to long jobs.

Original languageEnglish (US)
Title of host publicationProceedings of the 48th International Conference on Parallel Processing, ICPP 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450362955
DOIs
StatePublished - Aug 5 2019
Event48th International Conference on Parallel Processing, ICPP 2019 - Kyoto, Japan
Duration: Aug 5 2019Aug 8 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference48th International Conference on Parallel Processing, ICPP 2019
CountryJapan
CityKyoto
Period8/5/198/8/19

Keywords

  • Big data
  • Job scheduling
  • Resource management

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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