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Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Pingping Li*, Jiuxin Cao

School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China

* Corresponding Author: Pingping Li. Email: email

Computers, Materials & Continua 2023, 76(1), 81-105. https://doi.org/10.32604/cmc.2023.039076

Abstract

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

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APA Style
Li, P., Cao, J. (2023). Virtual machine consolidation with multi-step prediction and affinity-aware technique for energy-efficient cloud data centers. Computers, Materials & Continua, 76(1), 81-105. https://doi.org/10.32604/cmc.2023.039076
Vancouver Style
Li P, Cao J. Virtual machine consolidation with multi-step prediction and affinity-aware technique for energy-efficient cloud data centers. Comput Mater Contin. 2023;76(1):81-105 https://doi.org/10.32604/cmc.2023.039076
IEEE Style
P. Li and J. Cao, "Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers," Comput. Mater. Contin., vol. 76, no. 1, pp. 81-105. 2023. https://doi.org/10.32604/cmc.2023.039076



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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