Title: Genetic and static algorithm for task scheduling in cloud computing

Authors: Jocksam G. De Matos; Carla K. De M. Marques; Carlos H.P. Liberalino

Addresses: Department of Informatics, State University of Rio Grande do Norte, Mossoró, RN, Brazil ' Department of Informatics, State University of Rio Grande do Norte, Mossoró, RN, Brazil ' Department of Informatics, State University of Rio Grande do Norte, Mossoró, RN, Brazil

Abstract: Technological advancement has required ever more computing resources. In this context the cloud computing emerges as a new paradigm to meet this demand, though its resources are physically limited due to the growing data traffic that the system may be subject. The task scheduling aims to distribute tasks in order to make them more efficient in the use of computing resources. Thus, this paper aims to propose a solution to the task scheduling problem in cloud computing to reduce the processing time of the tasks and the number of virtual machines (VM). The metaheuristic genetic algorithm (GA) was used in the first stage of the algorithm, in order to reduce the processing time of the tasks. The static algorithm is designed to solve the set partitioning problem. Their performance was compared with two algorithms, classic and heuristic, along with realistic workloads.

Keywords: distributed computing; cloud computing; scheduling; metaheuristic.

DOI: 10.1504/IJCC.2019.10019206

International Journal of Cloud Computing, 2019 Vol.8 No.1, pp.1 - 19

Received: 17 Oct 2017
Accepted: 19 May 2018

Published online: 15 Feb 2019 *

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