A DAG Task Scheduling Scheme on Heterogeneous Computing Systems Using Invasive Weed Optimization Algorithm

Efficient task scheduling is crucial to heterogeneous cluster performance. And various scheduling methods based on random search technique have been proposed for both homogeneous and heterogeneous cluster systems. However, most of these methods have high computational overhead and poor convergence. Invasive weed optimization algorithm (IWO) is a novel bionic intelligent optimization algorithm that has fast convergence rate and easier implementation than traditional genetic algorithm (GA) based algorithm. In this paper, an IWO task scheduling (IWOTS) algorithm is proposed for heterogeneous cluster system. To the best of our knowledge, this study is the first time to apply IWO to discrete task scheduling problems. Extensive simulation experiment results show that IWOTS generally exhibits outstanding convergence performance and could produce an optimal scheduling strategy.