New Model and Genetic Algorithm for Multi-Installment Divisible-Load Scheduling

Abstract

The era of big data computing is coming. As scientific applications become more data intensive, finding an efficient scheduling strategy for massive computing in parallel and distributed systems has drawn increasingly attention. Most existing studies considered single-installment scheduling models, but very few literature involved multi-installment scheduling, especially in heterogeneous parallel and distributed systems. In this paper, we proposed a new model for periodic multi-installment divisible-load scheduling in which the make-span of the workload is minimized, and a genetic algorithm was designed to solve this model. Finally, experimental results show the effectiveness and efficiency of the proposed algorithm.