TY  - EJOU
AU  - Zhao, Mengkai 
AU  - Zhang, Zhixia 
AU  - Fan, Tian 
AU  - Guo, Wanwan 
AU  - Cui, Zhihua 

TI  - Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2023
VL  - 136
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Hybrid cloud environment; task scheduling; many-objective optimization model; many-objective optimization algorithm

DO  - 10.32604/cmes.2023.026671