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A WOA-based optimization approach for task scheduling in cloud Computing systems

Chen, Xuan, Cheng, Long orcid logoORCID: 0000-0003-1638-059X, Liu, Cong, Liu, Qingzhi, Liu, Jinwei, Mao, Ying and Murphy, John orcid logoORCID: 0000-0001-7822-1573 (2020) A WOA-based optimization approach for task scheduling in cloud Computing systems. IEEE Systems Journal . pp. 1-12.

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.
Item Type:Article (Published)
Uncontrolled Keywords:Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization;
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://dx.doi.org/10.1109/JSYST.2019.2960088
Copyright Information:© 2020 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:24294
Deposited On:20 Mar 2020 11:33 by Long Cheng . Last Modified 20 Mar 2020 12:07

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