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Top1. Introduction
Emerging trend of applications requires the demand of cloud environment and this term of cloud computing is defined by (Buyya, 2009) which is a parallel and distributed system with large number of virtualized computers where the provisioning of their resources is provided according to the mentioned SLA (Service Level Agreement) that has been signed between cloud service user and service provider. Virtualization is important features of cloud computing environment and it provides elasticity to the environment. Virtualization helps to reduce the cost of infrastructure and improve machine utilization. It is also an efficient technology for resource sharing in cloud environment. Different types of open source software framework are used for the implementation of virtualization, and hypervisor is one of them defined by (Nurmi et al., 2009). Hypervisor is a virtual machine manager that gives permission for multiple operating systems to run parallelly on one host machine and therefore, by allocating more virtual machines on a single host can minimize the cost of infrastructure as well as consumption of energy inside data center. Thus, the objective of current research is the minimizing the consumption of energy of datacenter by considering the performance of cloud environment and thus, the VM placement is an effective technique for this purpose.
These data centers consume more power than official buildings, city and house hold appliances. Cooling systems, network and computation processing are the main part of power consumption inside data centers. Data center consume 1.1% to 1.5% of overall electricity consumed in the world wide and which is growing 12% per year (Sharma et al., 2015). To manage and utilize the resources of data center in energy efficient way is a big challenge. Here, our study focuses the sub part of VM consolidation process i.e. VM placement process for the energy efficient management of resources.VM consolidation process helps to make decision of efficient VM placement by mapping virtual machines to appropriate servers defined by (Ferdaus et al., 2014). The process of VM consolidation can also minimize the consumption of energy of the data center by migrating virtual machines to some another host, replacement of VMs to reduce the load of host machine or by switching idle physical machines to low power states (Sharma et al., 2016). This energy consumption should be minimized without violating the SLA that specifies the quality of services (QOS) provided by cloud service providers. VM consolidation process uses different heuristics to select which virtual machine should be placed on which host machine. Dynamic placement of VMs on physical hosts depends upon the physical resources available on the host and resource requirement of the virtual machines.
Here, the main contributions of this paper are as follows:
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First we have discussed the VM placement technique, a sub part of VM consolidation process.
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We have compared various predefined VM placement techniques based on classical bin packing and analyzed that how these VM placement strategies affects the overall performance of cloud environment.
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We have proposed a decision making system for VM placement technique using one of the Evolutionary algorithms i.e. basic Genetic algorithms.
The layout of current manuscript is framed as, the section 2 presents the relevant work on VM placement along with various techniques used for virtual machines placement. Section 3 briefly describes about the previous predefined heuristic VM placement techniques along with the proposed model of VM placement Section 4 describes the framework or simulation environment and performance metrics that we have used for the implementation of VM consolidation process. Section 5 shows comparative analysis of various VM placement techniques and finally the manuscript is concluded in section 6 along with some future research directions.