Batch Mode Stochastic-Based Robust Dynamic Resource Allocation in a Heterogeneous Computing System

Heterogeneous, parallel and distributed computing systems frequently must operate in environments where uncertainty in system parameters is common. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In such an environment, the amount of processing required to complete any given task may fluctuate substantially due to variations in data size and content. Determining a resource allocation that accounts for this uncertainty is an important area of research. In this study, we define a stochastic robustness measure to facilitate batchmode resource allocation decisions in a dynamic environment where tasks are subject to individual deadlines and design a novel resource allocation technique that attempts to maximize our new stochastic robustness measure. We compare the performance of our technique against some commonly used approaches taken from the literature and adapted to our environment. Our performance results demonstrate the viability of our new technique in a dynamic heterogeneous computing system.