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A Resource Reduction Scheme with Low Migration Frequency for Virtual Machines on a Cloud Cluster
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  • A Resource Reduction Scheme with Low Migration Frequency for Virtual Machines on a Cloud Cluster
  • A Resource Reduction Scheme with Low Migration Frequency for Virtual Machines on a Cloud Cluster
저자명
Kim. Changhyeon,Lee. Wonjoo,Jeon. Changho
간행물명
KSII Transactions on internet and information systems : TIIS
권/호정보
2013년|7권 6호|pp.1398-1417 (20 pages)
발행정보
한국인터넷정보학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

A method is proposed to reduce excess resources from a virtual machine(VM) while avoiding subsequent migrations for a computer cluster that provides cloud service. The proposed scheme cuts down on the resources of a VM based on the probability that migration may occur after a reduction. First, it finds a VM that can be scaled down by analyzing the history of the resource usage. Then, the migration probability is calculated as a function of the VM resource usage trend and the trend error. Finally, the amount of resources needed to eliminate from an underutilized VM is determined such that the migration probability after the resource reduction is less than or equal to an acceptable migration probability. The acceptable migration probability, to be set by the cloud service provider, is a criterion to assign a weight to the resource reduction either to prevent VM migrations or to enhance VM utilization. The results of simulation show that the proposed scheme lowers migration frequency by 31.6~60.8% depending on the consistency of resource demand while losing VM utilization by 9.1~21.5% compared to other known approaches, such as the static and the prediction-based methods. It is also verified that the proposed scheme extends the elapsed time before the first occurrence of migration after resource reduction 1.1~2.3-fold. In addition, changes in migration frequency and VM utilization are analyzed with varying acceptable migration probabilities and the consistency of resource demand patterns. It is expected that the analysis results can help service providers choose a right value of the acceptable migration probability under various environments having different migration costs and operational costs.