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Energy-aware scheduling algorithm for precedence-constrained parallel tasks of network-intensive applications in a distributed homogeneous environment

A wide range of scheduling algorithms used in the data centers have traditionally concentrated on enhancement of performance metrics. Recently, with the rapid growth of data centers in terms of both size and number, the power consumption has become a major challenge for both industry and society. At the software level, energy-aware task scheduling is an effective technique for power reduction in the data centers. However, most of the currently proposed energy-aware scheduling approaches are only paying attention to computation cost. In the other words, they ignore the energy consumed by the network equipment, namely communication cost. In this paper, the problem of scheduling precedence-constrained parallel tasks of network-intensive applications on homogeneous physical machines in the data centers is addressed. The proposed Energy-Aware Scheduling algorithm (EASy) takes both the computation cost and communication cost into consideration with a low time complexity O(nlogn+2((e+n)mv)). The algorithm reduces energy consumption of the computation and communication by dynamic voltage frequency scaling (DVFS) and task packing respectively. The goal of EASy is to minimize the completion time besides energy consumption of the data center. The extensive experimental results using both synthetic benchmarks and real-world applications clearly demonstrate that EASy is capable of decreasing energy consumption of physical machines and network devices respectively by 4.5% and 15.06% on average.