Genetic Algorithms for Energy-Aware Scheduling in Computational Grids

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Provided by: North Dakota State University
Topic: Big Data
Format: PDF
Because of its sheer size, Computational Grids (CGs) require advanced methodologies and strategies to efficiently schedule user's tasks and applications to resources. Scheduling becomes even more challenging when energy efficiency, classical makespan criterion and user perceived Quality of Service (QoS) are treated as first-class objectives in CG resource allocation methodologies. In this paper, the authors approach the independent batch scheduling in CG as a biobjective minimization problem with makespan and energy consumption as the scheduling criteria. They use the Dynamic Voltage Scaling (DVS) methodology for reducing the cumulative power energy utilized by the system resources.
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