VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

• Published in: Journal of parallel and distributed computing Vol. 176; pp. 17 - 27
• Main Authors: Zhao, Hui, Feng, Nanzhi, Li, Jianhua, Zhang, Guobin, Wang, Jing, Wang, Quan, Wan, Bo
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.06.2023
• Subjects: Ant colony optimization; Cloud; Virtual machine migration; VM performance model; Ant colony optimization; Virtual machine migration; VM performance model; Cloud
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2023.02.003

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency. •VM Performance-Aware VMM method of improving users' experience and cloud service providers' benefits.•Optimizations include: maximizing VM performance, and minimizing the number of active PMs and the total migration cost.•An efficient VMM algorithm based on ant colony optimization.•Experimental results using both CloudSim platform and OpenStack-based cloud platform.