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.
Highlights
Cloud data centers are often characterized by high energy consumption when providing stable and reliable cloud services for cloud users
Improper Virtual machine (VM) placement leads to low resource utilization and high energy consumption
This paper addresses the issue of workload imbalance of various type resources on destination hosts during live VM migration
Summary
Cloud data centers are often characterized by high energy consumption when providing stable and reliable cloud services for cloud users. By defining the responsibility and availability among different samples, EEAP-VMP performs the affinity propagation of the accumulated evidence of responsibility and availability between PMs and VMs in the cloud data center. Such an affinity propagation algorithm continues until the destination hosts are found or a maximum of iterations is achieved. (2) We propose an affinity propagation algorithm to decide candidate destination hosts for VM placement, with the VOLUME 8, 2020 cumulative evidence for selecting destination hosts of the migrated VMs. In addition, an energy consumption calculation model is integrated to define the criterion for selecting a destination host for VM placement. The experimental results show that EEAP-VMP significantly improves QoS, reduces the numbers of VM migrations and the amount of running PMs, and decreases energy consumption of cloud data centers
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