On benchmarking task scheduling algorithms for heterogeneous computing systems

Abstract

The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. The proposed framework is general in nature. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.