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A hybrid algorithm for multi-objective task scheduling in heterogeneous cloud computing
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DOI：10.1007/s11227-025-07621-z.png)
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Abstract
En 中文
With an increasing complexity of the submitted tasks by users and the diversity of requirements, many computing platforms have begun to incorporate heterogeneous computing resources to meet user demands, which also brings new challenges to computing platform resource management. Task scheduling, as the core link of resource management, its optimization is particularly crucial. Efficient task scheduling strategies can not only significantly enhance the service experience of users, but also improve economic benefits for cloud service providers. In this paper, the grey wolf optimization (GWO) is improved which names as Improved-GWO; then, a novel hybrid algorithm called HIGWOLS is proposed to address the task scheduling problem for heterogeneous virtual machines. HIGWOLS is formed by combining the Improved-GWO with local search algorithm which improves the convergence speed and solution accuracy of traditional GWO. To validate the proposed hybrid method, a series of experiments are performed using different real world datasets with various sizes. The proposed hybrid method is compared with other swarm intelligent algorithms, such as PGSAO, GA-GWO and whale optimization algorithm (WOA). The proposed hybrid algorithm reduces the makespan by 8.9%, 24.6%, and 17.8% compared to PGSAO, GA-GWO and WOA, while improving resource utilization rate by 10.4%, 22.8%, and 27.5%, and reducing degree of load imbalance by 91.8%, 93.6%, and 90.9%, respectively. The experimental results indicate that the HIGWOLS has significant advantages when using HPC2N and NASA iPSC.
Keywords:
Cloud computing
Task scheduling
Multi-objective optimization
Hybrid algorithm
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