Contention Aware Workload and Resource Co-Scheduling on Power-Bounded Systems

Abstract

As power becomes a top challenge in HPC systems and data centers, how to sustain the system performance growth under limited available or permissible power becomes an important research topic. Traditionally, researchers have explored collocating non-interfering jobs on the same nodes to improve system performance. Nevertheless, power limits reduce the capacity of components, nodes, and systems, and induce or aggravate contention between jobs. Using prior power-oblivious job collocation strategies on power limited systems can adversely degrade system throughput. In this paper, we quantitatively estimate contention induced by power limits, and propose a Contention-Aware Power-bounded Scheduling (CAPS) for systems with finite power budgets. CAPS chooses to collocate jobs that are complementary when power is limited, and distributes the available power to nodes and components to minimize their interference. Experimental results show that CAPS improves system throughput and power efficiency by 10% or greater than power-oblivious job collocation strategies, depending on the available power, for hybrid MPI/OpenMP benchmarks on a 192-core 8-node cluster.