Performance Evaluation of Overload Control in Multi-cluster Grids

Abstract

Multi-cluster grids are widely employed to execute workloads consisting of compute- and data-intensive applications in both research and production environments. Such workloads, especially when they are bursty, may stress shared system resources, to the point where overload conditions occur. Overloads can severely degrade the system performance and responsiveness, potentially causing user dissatisfaction and perhaps even revenue loss. However, the characteristics of multi-cluster grids, such as their complexity and heterogeneity, raise numerous nontrivial issues while controlling overload in such systems. In this work we present an extensive performance evaluation of overload control in multi-cluster grids. We adapt a dynamic throttling mechanism that enforces a concurrency limit indicating the maximum number of tasks running concurrently for every application. Using diverse workloads we evaluate several throttling mechanisms including our dynamic mechanism in our DAS-3 multi-cluster grid. Our results show that throttling can be used for effective overload control in multi-cluster grids, and in particular, that our dynamic technique improves the application performance by as much as 50% while also improving the system responsiveness by up to 80%.