Migration in initial and dynamic virtual machine placement algorithms

Abstract

Cloud computing provides a computing platform for the users to meet their demands in an efficient way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines are utilized to satisfy the user needs and are placed on physical machines of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of physical machines used helps in cutting down the power consumption by substantial amount. An optimal technique is to map virtual machines to physical machines such that the number of required physical machines is minimized. The virtual machine placement problem with the target of minimizing the total energy consumption by the running of physical machines, which is also an indication of increasing resource utilization and reducing cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of physical machines, a multi-dimensional normalized resource cube is presented. Based on this model, we propose a virtual machine placement algorithm with migration support which can balance the utilization of multi-dimensional resources, reduce the number of running physical machines and thus lower the energy consumption. We also evaluate our proposed algorithm via extensive simulations and experiments on Cloudsim. Experimental results show, over the long run, proposed algorithm can save as much as 15% energy than the other algorithms.