Energy-Aware Scheduling of Workflow in Cloud Center with Deadline Constraint

Abstract

Cloud computing is a scale-based platform, provides several kinds flexible resources such as storage, databases and computing power, requires more virtual machines(VMs) and consumes lots of electricity resource, which is suitable to execute workflow applications. However, with the increasing scale of data centers, the energy consumption problem has become one of the major concerns in clouds. To address the issue of energy consumption optimization, a heuristic approach named deadline constrained energy-aware scheduling algorithm(EAS) for scheduling a workflow application is proposed in this paper. Based on the proposed heuristic policy, two energy efficient sub-algorithms are implemented: task mapping algorithm and task merge algorithm. Experiments are conducted to investigate the performance of the proposed algorithms and results show that the proposed algorithms can reduce energy consumption significantly.