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Article type: Research Article
Authors: Wang, Yu-Lin | Wang, Jin-Heng; *
Affiliations: School of Computer Science & Engineering, Guangzhou Institute of Science and Technology, Guangzhou, Guangdong, China
Correspondence: [*] Corresponding author. Jin-Heng Wang, School of Computer Science & Engineering, Guangzhou Institute of Science and Technology, Guangzhou, Guangdong 510540, China. Email: [email protected].
Abstract: Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration.
Keywords: Cloud computing, virtual machine consolidation, improved K-nearest neighbor regression, earth mover’s distance, attribute weighting
DOI: 10.3233/JIFS-239851
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10315-10328, 2024
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