Exploiting Application Tunability for Efficient, Predictable Resource Management in Parallel and Distributed Systems

Abstract

Parallel and distributed computing is becoming increasingly mainstream, driven both by the widespread availability of commodity small-scale symmetric multiprocessors and high-performance cluster platforms, as well as the growing use of parallelism and distribution in networked applications such as image recognition, media processing, virtual reality, and telepresence. However, many of these applications impose soft timeliness and output quality constraints on top of the traditional performance requirements, necessitating efficient, predictable management of system resources. Existing techniques are inadequate to simultaneously support these twin requirements of efficiency and predictability. In this paper, we propose a novel approach for increasing system efficiency while meeting application timeliness and quality constraints. Our approach exploits the application tunability found in many general-purpose computations. Tunability refers to an application's ability to trade off resource requirements over several dimensions including time, quality, and resource type; the resulting flexibility enables the underlying resource management system to choose an application operating point best suited to available resource characteristics. We describe language and scheduler extensions to support tunability in the MILAN metacomputing environment and then systematically characterize performance benefits of tunability using a parameterizable task system. Our results show that application tunability is easily expressible and can significantly improve resource utilization.