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

13 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
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 28 Aug 2020

Fingerprint Dive into the research topics of 'Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center'. Together they form a unique fingerprint.

Cite this