Hybrid Performance-Oriented Scheduling of Moldable Jobs with QoS Demands in Multiclusters and Grids

Ligang He*, Stephen A. Jarvis, Daniel P. Spooner, Xinuo Chen, Graham R. Nudd

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Citations (Scopus)

Abstract

This paper addresses the dynamic scheduling of moldable jobs with QoS demands (soft-deadlines) in multiclusters. A moldable job can be run on a variable number of resources. Three metrics (over-deadline, makespan and idle-time) are combined with weights to evaluate the scheduling performance. Two levels of performance optimisation are applied in the multicluster. At the multicluster level, a scheduler (which we call MUSCLE) allocates parallel jobs with high packing potential to the same cluster; MUSCLE also takes the jobs' QoS requirements into account and employs a heuristic to achieve performance balancing across the multicluster. At the single cluster level, an existing workload manager, called TITAN, utilizes a genetic algorithm to further improve the scheduling performance of the jobs allocated by MUSCLE. Extensive experimental studies are conducted to verify the effectiveness of the scheduling mechanism in MUSCLE. The results show that the comprehensive scheduling performance of parallel jobs is significantly improved across the multicluster.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsHai Jin, Jianhua Sun, Yi Pan, Nong Xiao
PublisherSpringer Verlag
Pages217-224
Number of pages8
ISBN (Print)3540235647, 9783540235644
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3251
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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