Energy-aware Scheduling of MapReduce Jobs for Big Data Applications

Energy-aware Scheduling of MapReduce Jobs for Big Data Applications

                                Energy-aware Scheduling of MapReduce Jobs for Big Data Applications.The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its opensource implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center’s overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA). We first model the problem of energy-aware scheduling of a single MapReduce job as an Integer Program.



We then propose two heuristic algorithms, called Energy-aware MapReduce Scheduling Algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in order to minimize the energy consumed when executing the application. We perform extensive experiments on a Hadoop cluster to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite including TeraSort, PageRank, and K-means Clustering, and then use this data in an extensive simulation study to analyze the performance of the proposed algorithms. The results show that EMRSA-I and EMRSA-II are able to find near optimal job schedules consuming approximately 40% less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.

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