Prediction-Based Independent Task Scheduling for Heterogeneous Distributed Computing Systems

Abstract

Independent task scheduling algorithms in distributed computing systems deal with three main conflicting factors including load balance, task execution time and scheduling cost. In this paper, the problem of scheduling tasks arriving at a low rate and with long execution time in heterogeneous computing systems is studied, and a new scheduling algorithm based on prediction is proposed. This algorithm evaluates the utility of task scheduling based on statistics and prediction to solve the influence of heterogeneous computing systems. The experimental results reveal that the proposed algorithm adequately balances the conflicting factors, and thus performs better than some classical algorithms such as MCT and MET when the parameters are well selected.