Open Access

ARTICLE

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Mengkai Zhao¹, Zhixia Zhang², Tian Fan¹, Wanwan Guo¹, Zhihua Cui^1,*

1 The School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
2 The School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

* Corresponding Author: Zhihua Cui. Email:

Computer Modeling in Engineering & Sciences 2023, 136(3), 2425-2450. https://doi.org/10.32604/cmes.2023.026671

Received 19 September 2022; Accepted 06 December 2022; Issue published 09 March 2023

Abstract

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving. The algorithm uses opposition-based learning to generate initial populations for faster convergence. Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range. By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.

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Keywords

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