An Energy Efficient Algorithm for Big Data Processing in Heterogeneous Cluster

It is reported that the electricity cost to operate a cluster may well exceed its acquisition cost, and the processing of big data requires large scale cluster and long period. Therefore, energy efficient processing of big data is essential for the data owners and users, and it is also a great challenge for the energy use and environment protection. Existing methods powered down some nodes to reduce energy consumption or developed new strategies of data storage in the cluster. However, we can find that much energy is still wasted even minimal nodes are used to process the task, and new storage strategies do not suit for the deployed clusters for the extra cost of data transformation. In this paper, we propose a novel algorithm MinBalance to processing I/O intensive big data tasks energy efficiently in heterogeneous cluster. The algorithm can be divided into two steps, node selection and workload balance. In the former step, four greedy policies are used to select the proper nodes considering heterogeneity of the cluster. While in the latter step, the workloads of the selected nodes will be well balanced to avoid the energy wastes caused by waiting. MinBalance is a universal algorithm and cannot be affected by the data storage strategies. Experimental results indicate that MinBalance can achieve over 60% energy reduction for large data sets over the traditional methods of powering down partial nodes.