Energy-aware processing of big data in homogeneous cluster. Issue 2 (February 2017) - Ding, Youwei; Qin, Xiaolin; Zhou, Qian; Liu, Liang; Wang, Taochun
Abstract The size of data centers is becoming larger to deal with the exponential data growth, and the energy consumption challenges the services providers and the environment. Various data placement strategies were developed to reduce the energy consumption of processing big data on the level of storage system, but they were typically developed for specific applications and storage medium. This paper proposes an energy-aware algorithm EABD of processing big data in homogeneous cluster with general data storage. We show that a variation of this optimization can be reduced to set cover problem, and a heuristic algorithm is proposed to reduce the energy consumption by selecting proper nodes and assigning balanced workload to each selected node. This algorithm will not be influenced by the data placement strategies and storage medium. Simulation results show that our algorithm significantly reduces energy consumption in different situations.