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.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1D1A1B07045838 and NRF-2016R1D1A3B03933370). The author to whom correspondence should be addressed is Joon-Min Gil.
© 2018 KIPS.
- Cloud computing
- Computational intelligence
- Resource monitoring
ASJC Scopus subject areas
- Information Systems