A Survey of Virtual Machine Placement Techniques and VM Selection Policies in Cloud Datacenter

The large scale virtualized data centers have been established due to the requirement of rapid growth in computational power driven by cloud computing model . The high energy consumption of such data centers is becoming more and more serious problem .In order to reduce the energy consumption, server consolidation techniques are used .But aggressive consolidation of VMs can lead to performance degradation. Hence another problem arise that is, the Service Level Agreement(SLA) violation. The optimized consolidation is achieved through optimized VM placement and VM selection policies . A comparative study of virtual machine placement and VM selection policies are presented in this paper for improving the energy efficiency

On Improving The Performance And Resource Utilization of Consolidated Virtual Machines: Measurement, Modeling, Analysis, and Prediction

This dissertation addresses the performance related issues of consolidated \emph{Virtual Machines} (VMs). \emph{Virtualization} is an important technology for the \emph{Cloud} and data centers. Essential features of a data center like the fault tolerance, high-availability, and \emph{pay-as-you-go} model of services are implemented with the help of VMs. Cloud had become one of the significant innovations over the past decade. Research has been going on the deployment of newer and diverse set of applications like the \emph{High-Performance Computing} (HPC), and parallel applications on the Cloud. The primary method to increase the server resource utilization is VM consolidation, running as many VMs as possible on a server is the key to improving the resource utilization. On the other hand, consolidating too many VMs on a server can degrade the performance of all VMs. Therefore, it is necessary to measure, analyze and find ways to predict the performance variation of consolidated VMs. This dissertation investigates the causes of performance variation of consolidated VMs; the relationship between the resource contention and consolidation performance, and ways to predict the performance variation. Experiments have been conducted with real virtualized servers without using any simulation. All the results presented here are real system data. In this dissertation, a methodology is introduced to do the experiments with a large number of tasks and VMs; it is called the \emph{Incremental Consolidation Benchmarking Method} (ICBM). The experiments have been done with different types of resource-intensive tasks, parallel workflow, and VMs. Furthermore, to experiment with a large number of VMs and collect the data; a scheduling framework is also designed and implemented. Experimental results are presented to demonstrate the efficiency of the ICBM and framework

Performance Impact of Virtual Machine Placement in a Datacenter

ABSTRACT- In virtualized systems, several Virtual Machines (VM) running on a single hardware platform share and compete for the hardware resources such as memory, disk and network IO to meet a certain Quality of Service (QoS) requirements. It is critical to characterize and understand how the different workloads running in the VMs interact and share such resources to be able to map them efficiently onto processor cores and server hosts for optimal performance. This is especially important for resources such as memory controllers or the on-chip or intersocket networks for which there is currently no software control. In this paper, we present a measurement-based performance analysis of server virtualization workloads from a real system using virtual machines that are part of the popular industry standard VMmark benchmark, a server consolidation benchmark. First, we characterize the relative resource contention and interference impact of VMs when multiple virtual workloads are run together. Second, we study the effects of co-locating different types of VMs under various VM to core placement schemes and discover the best placement for performance. We observe performance variations from 25-65 % for Database servers and from 7-40 % for File servers when compared to standalone VM depending on the placement of these VMs onto cores and the degree of sharing of resources. Finally, we propose an interference metric and regression model for the worst set of colocated VMs in our study. Based on different VM placement schemes we show that the overall server consolidation performance in a virtualized host can be improved by 8 % when the VMs are placed effectively