A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

Yu Cai (Michigan Technological University, Houghton, USA)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJGC.2017070102

Abstract

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.
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There is an expansion in research on energy efficiency in a large-scale data center or server clusters in the past few years. In this section, we only review the work related to VM management and cloud computing since they are more closely related to this work.

One most important technology that makes cloud computing possible is the use of virtualization (He et al., 2016; Clark et al., 2005; Wan et al., 2016, Wood et al., 2007). Virtualization allows consolidation of a number of smaller workloads into partitions of a larger physical server, while the user achieves the same level of performance and security at a lower management cost and possibly lower hardware cost (Verma et al., 2008, Deshpande et al., 2016).

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