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Online Multi-User Workflow Scheduling Algorithm for Fairness and Energy Optimization

Abstract : This article tackles the problem of scheduling multiuser scientific workflows with unpredictable random arrivals and uncertain task execution times in a Cloud environment from the Cloud provider point of view. The solution consists in a deadline sensitive online algorithm, named NEARDEADLINE, that optimizes two metrics: the energy consumption and the fairness between users. Scheduling workflows in a private Cloud environment is a difficult optimization problem as capacity constraints must be fulfilled additionally to dependencies constraints between tasks of the workflows. Furthermore, NEARDEADLINE is built upon a new workflow execution platform. As far as we know no existing work tries to combine both energy consumption and fairness metrics in their optimization problem. The experiments conducted on a real infrastructure (clusters of Grid'5000) demonstrate that the NEARDEADLINE algorithm offers real benefits in reducing energy consumption, and enhancing user fairness.
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Contributor : Emile Cadorel <>
Submitted on : Tuesday, June 16, 2020 - 10:23:52 AM
Last modification on : Tuesday, January 5, 2021 - 4:26:24 PM

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Emile Cadorel, Hélène Coullon, Jean-Marc Menaud. Online Multi-User Workflow Scheduling Algorithm for Fairness and Energy Optimization. CCGrid2020 : 20th International Symposium on Cluster, Cloud and Internet Computing, Nov 2020, Melbourne, Australia. ⟨10.1109/CCGrid49817.2020.00-36⟩. ⟨hal-02551733⟩

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