A slowdown model for applications executing on time-shared clusters of workstations

Abstract

Distributed applications executing on clustered environments typically share resources (computers and network links) with other applications. In such systems, application execution may be retarded by the competition for these shared resources. In this paper, we define a model that calculates the slowdown imposed on applications in time-shared multi-user clusters. Our model focuses on three kinds of slowdown: local slowdown, which synthesizes the effect of contention for CPU in a single workstation; communication slowdown, which synthesizes the effect of contention for the workstations and network links on communication costs; and aggregate slowdown, which determines the effect of contention on a parallel task caused by other applications executing on the entire cluster, i.e., on the nodes used by the parallel application. We verify empirically that this model provides an accurate estimate of application performance for a set of compute-intensive parallel applications on different clusters with a variety of emulated loads.