Genetic Algorithms for Energy-Aware Scheduling in Computational Grids

Abstract

Because of its sheer size, Computational Grids (CGs) require advanced methodologies and strategies to efficiently schedule users tasks and applications to resources. Scheduling becomes even more challenging when energy efficiency, classical make span criterion and user perceived Quality of Service (QoS) are treated as first-class objectives in CG resource allocation methodologies. In this paper we approach the independent batch scheduling in CG as a biobjective minimization problem with make span and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for reducing the cumulative power energy utilized by the system resources. We develop two Genetic Algorithms (GAs) with elitist and struggle replacement mechanisms as energy-aware schedulers. The proposed algorithms were experimentally evaluated for four CG size scenarios in static and dynamic modes. The simulation results showed that our proposed GA-based schedulers fairly reduce the energy usage to a level that is sufficient to maintain the desired quality level(-s)