Article Preview
Top1. Introduction
The distributed computing has evolved into many popular forms in recent times. These forms include technologies such as cluster computing, grid computing and cloud computing. Several recent articles have established the importance and dominance of cloud computing paradigm in present scenario. Many small and big enterprises are migrating to SaaS and IaaS for alternates of licensing and expensive hardware. The sheer imagination of volume of communication, networking, hosting and development involved at cloud platforms reminds us the challenges in cloud computing. Several important aspects of cloud such as energy efficiency, security, resource utilization, Virtual machine (VM) Allocation, migration and placement, load balancing etc. have started to bother users and developers. Recent works on these aspects establishes the importance of these functional and operational parameters.
Cloud environment uses concept of data center for hardware resources, where VMs are executable instances. VMs are created on hosts as a result of computing and storage requirements posed by executing processes. Each host in general can support a predetermined or adaptively determined utilization levels. An approach called live migration of VMs is generally used to achieve load balancing, improved utilization of under-utilized hosts and improve energy efficiency of the total system. Generally, a list of VMs is created for migration. This list of VMs must be placed on most suitable hosts. In recent times also, several proposals have considered VM Placement (VMP) problem. VMP seems to be most basic problem but it is a multidimensional problem defined by various constraints, such as performance tuning (Kusic et al., 2009), scalability (Piao & Yan, 2010), availability (Bin et al., 2011), network (Wang et al., 2011) (Jiang et al., 2012), cost (Sharma et al., 2011), etc. Existing proposals (Li et al., 2013) establish the importance of the VMP problem and energy saving policy. Moreover, one can differentiate between the approaches such as mono objective optimization (Caron et al., 2013; Chaisiri et al., 2009; Chang et al., 2013; Biran et al., 2012; Dang & Hermenier, 2013; Dias & Costa, 2012) and multi-objective optimization (Adamuthe et al., 2013; Anand et al., 2013; Dong & Herbert, 2013; Dong et al., 2013; Dong et al., 2013a; S. Fang et al., 2013; W. Fang et al., 2013a; Ferreto et al., 2011; Gao et al., 2013).