A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

• Published in: IEEE access 2020, Vol.8, p.228639-228654
• Main Authors: Shao, Yifan, Yang, Qiangqiang, Gu, Yajuan, Pan, Yong, Zhou, Yi, Zhou, Ziao
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud computing; Consolidation; Data centers; discrete particle swarm optimization; Dynamic VM consolidation; energy awareness; Energy consumption; Energy management; gray model; Grey prediction; Heuristic algorithms; Load modeling; Particle swarm optimization; Placement; Prediction algorithms; Predictive models; Quality of service; service quality; Strategy; Virtual environments
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3046318

With the continuous expansion of the scale of cloud datacenters, high energy consumption has become a problem. Virtual machine (VM) consolidation is an effective method of energy management, but overly aggressive consolidation has caused issues such as high service level agreement violation (SLA) rates and excessive migrations. To reduce energy consumption while ensuring the quality of service of a datacenter, a VM dynamic consolidation strategy based on a gray model and an improved discrete particle swarm algorithm (GM-DPSO) is proposed. This strategy uses a gray prediction algorithm for load detection and adjusts the size of the threshold according to the load condition. Then, we select VMs for overloaded and underloaded hosts and complete the placement of VMs through a VM placement strategy based on an improved discrete particle swarm algorithm. This method aims to improve the load balance, establish a mapping between VMs and hosts, use particles to search for the best VM placement in the global scope, and reduce the probability of SLAV. Our algorithm considers the impact of VM migration on system energy consumption and service quality and takes sufficient measures to reduce the number of migrations. To verify the effectiveness and practicality of this strategy, experiments were conducted on actual workloads, and the results were compared with those of other strategies. The experimental results show that the GM-DPSO greatly improves service quality while reducing energy consumption and SLAV by 34.53% and 97.53%, respectively. The methods and theories in this paper provide strong theoretical and practical engineering guidance for large-scale cloud deployment.