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

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.