Group-wise performance evaluation of processor co-allocation in multi-cluster systems

Abstract

Performance evaluation in multi-cluster processor co-allocation - like in many other parallel job scheduling problems - is mostly done by computing the average metric value for the entire job stream. This does not give a comprehensive understanding of the relative performance of the different jobs grouped by their characteristics. It is however the characteristics that affect how easy/hard jobs are to schedule. We, therefore, do not get to understand scheduler performance at job type level. In this paper, we study the performance of multi-cluster processor co-allocation for different job groups grouped by their size, components and widest component. We study their relative performance, sensitivity to parameters and how their performance is affected by the heuristics used to break them up into components. We show that the widest component use characteristic that most affects job schedulability. We also show that to get better performance, jobs should be broken up in such a way that the width of the widest component is minimized.