Many-objective performance enhancement in computing clusters

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches.