Title: Executing time and cost-aware task scheduling in hybrid cloud using a modified DE algorithm

Authors: Yuanyuan Fan; Qingzhong Liang; Yunsong Chen; Xuesong Yan

Addresses: School of Computer Science, China University of Geosciences, Wuhan, Hubei, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

Abstract: Task scheduling is one of the basic problems in cloud computing. In hybrid cloud, tasks scheduling faces new challenges. In this paper, we propose a GaDE algorithm, based on differential evolution algorithm, to improve single objective scheduling performance of a hybrid cloud. In order to better deal with the multi-objective task scheduling optimisation in hybrid clouds, on the basis of the GaDE and Pareto optimum of quick sorting method, we present a multi-objective algorithm, named NSjDE. This algorithm also makes considerations to reduce the frequency of evaluation. Comparing with experiments of Min-Min algorithm, GaDE algorithm and NSjDE algorithm, results show that for the single object task scheduling, GaDE and NsjDE algorithms perform better in getting the approximate optimal solution. The optimisation speed of multi-objective NSjDE algorithm is faster than the single-objective jDE algorithm, and NSjDE can produce more than one non-dominated solution meeting the requirements, in order to provide more options to the user.

Keywords: hybrid cloud; task scheduling; executing time-aware; cost-aware.

DOI: 10.1504/IJCSE.2019.098532

International Journal of Computational Science and Engineering, 2019 Vol.18 No.3, pp.217 - 226

Received: 27 Apr 2016
Accepted: 30 Aug 2016

Published online: 12 Mar 2019 *

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