Adaptive resource allocation with job runtime uncertainty

Abstract

In this paper, we address the problem of dynamic resource allocation in presence of job run- time uncertainty. We develop an execution delay model for runtime prediction, and design an adaptive stochastic allocation strategy, named Pareto Fractal Flow Predictor (PFFP). We conduct a comprehensive performance evaluation study of the PFFP strategy on real production traces, and compare it with other well-known non-clairvoyant strategies over two metrics. In order to choose the best strategy, we perform bi-objective analysis according to a degradation methodology. To analyze possible biasing results and negative effects of allowing a small portion of theproblem instances with large deviation to dominate the conclusions, we present performance profiles of the strategies. We show that PFFP performs well in different scenarios with a variety of workloads and distributed resources.