An Intelligent Residual Resource Monitoring Scheme in Cloud Computing Environments

JongBeom Lim, HeonChang Yu and Joon-Min Gil
Volume: 14, No: 6, Page: 1480 ~ 1493, Year: 2018
10.3745/JIPS.02.0102
Keywords: Cloud Computing, Clustering, Computational Intelligence, Resource Monitoring
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Abstract
Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine’s ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

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Cite this article
IEEE Style
JongBeom Lim, HeonChang Yu, and Joon-Min Gil, "An Intelligent Residual Resource Monitoring Scheme in Cloud Computing Environments," Journal of Information Processing Systems, vol. 14, no. 6, pp. 1480~1493, 2018. DOI: 10.3745/JIPS.02.0102.

ACM Style
JongBeom Lim, HeonChang Yu, and Joon-Min Gil, "An Intelligent Residual Resource Monitoring Scheme in Cloud Computing Environments," Journal of Information Processing Systems, 14, 6, (2018), 1480~1493. DOI: 10.3745/JIPS.02.0102.