A Novel Discrete Bi-objective Optimization Method for Virtual Machine Placement

Abstract

According to the cloud computing paradigm, cloud providers can offer computing infrastructure as a service in virtual machines (VMs) running on physical machines (PMs). A data center relies on a VM placement (VMP) algorithm to allocate VMs to the appropriate PMs. As VMs are running on PMs, cloud services providers need to consider operating costs and minimize energy consumption to reduce costs. Meanwhile, multiple VMs running on fewer PMs will result in excellent resource contention, affecting the user experience. How to reduce energy consumption while maintaining VM performance remains a challenge. Although some existing VMPs study VM performance degradation, they do not consider the PM’s state, which will result in errors occurring when predicting VM performance. Moreover, many current VMP algorithms converge too slowly and easily fall into the local optimum solutions. So in this paper, first, we build the energy consumption model based on real data sets to obtain more accurate energy consumption values. Second, we investigate and model VM performance in CPU and memory. Third, we formulate the VMP as a discrete optimization problem based on the energy consumption model and VM performance model. We then propose a novel bi-objective discrete VMP (BDVMP) algorithm to solve it. Finally, we evaluate the BDVMP algorithm on both the CloudSim platform and the real Openstack platform. The results show the efficiency of our BDVMP algorithm.