Lore: A Learning-based Approach forWorkflow Scheduling in Clouds

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The workflow scheduling problem is a critical challenge in clouds. Meticulously designed heuristics have attempted to address the intricate decision problem at a high cost. A more general approach is expected to handle different types of workflows and resource configurations. In this paper, a deep reinforcement Learning based apprOach for woRkflow schEduling (Lore) in clouds has been proposed to minimize the completion time of workflows. Moreover, Monte Carlo Tree Search and graph convolutional network are applied to improve performance further. Experimental results show that Lore outperforms the baselines, reducing average makespan by 2 - 10%, and enabling resource utilization increase by up to 20%.

Original languageEnglish
Title of host publicationProceedings of the 2022 Research in Adaptive and Convergent Systems, RACS 2022
PublisherAssociation for Computing Machinery
Pages47-52
Number of pages6
ISBN (Electronic)9781450393980
DOIs
Publication statusPublished - 3 Oct 2022
Event2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022 - Virtual, Online, Japan
Duration: 3 Oct 20226 Oct 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022
Country/TerritoryJapan
CityVirtual, Online
Period3/10/226/10/22

Keywords

  • deep reinforcement learning
  • graph convolutional network
  • monte carlo tree search
  • workflow scheduling

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