A heuristic task scheduling algorithm based on server power efficiency model in cloud environments

As the ever-growing energy consumption rises as a global concern, energy conservation in cloud data centers has become a topic of interest. Based on extensive research and experimental analysis of the energy consumption characteristics and performance records from SPEC data set, this paper presents a power efficiency model for cloud servers. With the model, we use server power efficiency to guide task scheduling and propose a heuristic task scheduling algorithm (ECOTS) for optimizing energy consumption in cloud environments. ECOTS takes into account multiple key factors such as task resource requirements, server power efficiency model and performance degradation in order to reduce system energy consumption at a minimal cost of performance. ECOTS algorithm has low time and space complexity with a better global searching ability to approach the optimal scheduling plan. We simulated a heterogeneous cluster environment and conducted experiments to evaluate the effectiveness of ECOTS. Simulation results show that ECOTS algorithm is the most energy-efficient without violating all the cloud tasks' resources requirement. Moreover, ECOTS algorithm effectively reduces total energy consumption by more than 20% compared with the Improvement of Min-Min algorithm. (C) 2017 Elsevier Inc. All rights reserved.