Exploiting a Synergy between Greedy Approach and NSGA for Scheduling in Computing Clusters

A. S. M. Rizvi, Tarik Reza Toha, Siddhartha Shankar Das, Sriram Chellappan, A. B. M. Alim Al Islam

Abstract: Computing clusters are evaluated using different performance metrics, which often appear to be conflicting while being attempted to be optimized. For such conflicting cases along with frequently having an existence of heterogeneous environment, it is difficult for the cluster administrators to efficiently schedule machines, i.e., to select the right number and right combination of machines. In this paper, we develop a technique through which cluster administrators can select the right set of machines to enhance energy efficiency and cluster performance. To do so, first, we perform extensive laboratory experiments for a period of more than one year. Based on empirical analyses of data collected from the experiments, we formulate a many-objective optimization problem for clusters and integrate a greedy approach with Non-dominated Sorting Genetic Algorithm (NSGA-III) to solve this problem. We demonstrate that our approach mostly performs better than existing approaches in the literature through both real experimentation and simulation.