Abstract
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.
Highlights
With the continuous development of cloud computing technology, the scale of cloud datacenters is expanding
We propose a dynamic virtual machine (VM) consolidation (GM-discrete particle swarm optimization (DPSO)) strategy based on a gray model and an improved discrete particle swarm algorithm
We select the VMs to be migrated according to the migration times and CPU utilization levels and complete the consolidation with a VM placement strategy based on the improved discrete particle swarm algorithm
Summary
With the continuous development of cloud computing technology, the scale of cloud datacenters is expanding. Y. Shao et al.: A dynamic VM resource consolidation strategy based on gray model and improved discrete particle swarm optimization. VM consolidation is a dynamic method that decreases the number of hosts operating in a datacenter through VM migrations to gain the full benefit of computing resources and reduce energy consumption [11], [12]. According to the above analysis, this paper proposes a VM dynamic consolidation strategy called GM-DPSO based on a gray model and improved discrete particle swarm optimization. The work performed in this article is as follows: 1) We use dual thresholds for load detection and introduce gray prediction and dynamic threshold adjustment to optimize host overload detection and reduce the migration of VMs. 2) A VM selection strategy based on the migration time and CPU utilization is proposed to decrease the migration time and frequency.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
