Hybrid Bio-inspired Scheduling Algorithms for Batch of Tasks on Heterogeneous Computing System

Abstract

Due to high operational cost, the problem of scheduling a batch of tasks (BoT) applications on heterogeneous computing system (HCS) remains a challenging problem. Accordingly, a plethora of evolutionary algorithms (EAs) and non-EAs have been proposed as solutions. Due to the ability of exploration of major solution space, EAs have been proven to be very effective in addressing the job scheduling problem. This work proposes two hybrid bio-inspired scheduling algorithms VPG and VDG featuring the combined best properties of VNS, PSO, DE and GA. The expected-time-to-compute (ETC) benchmark have been used to first present the performance of eight non-EAs viz. MCT, MinMin, MaxMin, Sufferage, HLTF, relative cost, MINMin and MINSuff in terms of makespan and energy consumption. The study is then extended to evaluate the performance of VPG, VDG and their seeded variants with GA, PSO and DE. Simulation study establishes the superior performance of VDG over peers.