Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

Zhihua Li, Shujie Guo, Lei Yu, Victor Chang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
32 Downloads (Pure)

Abstract

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated.
Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.
Original languageEnglish
Pages (from-to)158356-158368
Number of pages13
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 28 Aug 2020

Bibliographical note

Funding Information:
This work was supported in part by the Smart Manufacturing New Model Application Project Ministry of Industry and Information Technology under Grant ZH-XZ-18004, in part by the Future Research Projects Funds for the Science and Technology Department of Jiangsu Province under Grant BY2013015-23, in part by the Fundamental Research Funds for the Ministry of Education under Grant JUSRP211A 41, in part by the Fundamental Research Funds for the Central Universities under Grant JUSRP42003, in part by the 111 Project under Grant B2018, and in part by the VC Research under Grant VCR 0000071.

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

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