Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Intelligent Residual Resource Monitoring Scheme in Cloud Computing Environments

  • Lim, JongBeom (Dept. of Game & Multimedia Engineering, Korea Polytechnic University) ;
  • Yu, HeonChang (Dept. of Computer Science & Engineering, Korea University) ;
  • Gil, Joon-Min (School of Information Technology Engineering, Daegu Catholic University)
  • Received : 2018.08.31
  • Accepted : 2018.10.22
  • Published : 2018.12.31
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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.

Keywords

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Fig. 1. Performance degradation problem after virtual machine migration.

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Fig. 2. The monitoring architecture in cloud computing environments.

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Fig. 3. Relative performance indices of physical machines.

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Fig. 4. Difference in the relative performance indices before versus after finding the target migration machine with the proposed scheme.

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Fig. 5. The count used to select the target machine from the available nodes.

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Fig. 6. Difference in the relative performance indices before versus after finding the target migration machine without the proposed scheme.

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Fig. 7. Difference in the relative performance indices with versus without the proposed scheme.

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Fig. 8. Service level agreement violation without the proposed scheme.

Table 1. Experimental settings

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