Comparative Study of Virtual Machine Placement Algorithms in Cloud Computing Environment


  • In traditional data centers, a numbers of services run onto the dedicated physical servers. These data centers are not used to their full capacity in terms of resource utilization.
  • Virtualization allows the movement of virtual machine from one host to the another host, which is called virtual machine migration.
  • Virtual machine placement is the part of the virtual machine migration. Mapping of virtual machines to the physical machines is called the virtual machine placement.
  • Most of the virtual machine placement algorithms try to achieve some goal. This goal can either saving energy by shutting down some severs or it can be maximizing the resource utilization.
  • Four steps are involved in the virtual machine migration process. First step is to select the physical machine which is overloaded or under loaded, next step is to select one or more virtual machines, and then select the physical machine where selected virtual machine can be placed and last step is to transfer the virtual machine.
  • Selecting the suitable host is one of the challenging task in the migration process, because wrong selection of host can increase the number of migration, resource wastage and energy consumption.
  • This paper only focuses to the third step that is selecting a suitable physical machine that can host the virtual machine. It shows an analysis of different existing virtual machine placement algorithms with their anomalies.




A. Constraint Programming

In this technique some constraints are applied and these constraints must be fulfilled on relations between variables. The VM placement problem can be designed as a constraint programming problem. These constraints could be Capacity, SLA, QoS.

B. Bin Packing

The VM placement problem can be designed as a bin packing problem. The PMs can be considered as bins and the VMs to be placed can be considered as objects to be filled in the bins. The aim is to place as many VMs into a single PM so that number of PM required to pack the VMs is minimized.

C. Stochastic Integer Programming

Stochastic Integer Programming is used to optimize the problems, which involves uncertainty. VM placement problem can be considered as a Stochastic Integer Programming because resource demand of the VM are known or it can be estimated and the objective is to find the suitable host which consume less energy and minimize the resources wastage.


D. Genetic Algorithm

A GA is used to find exact or approximate solutions to optimization and search problems. The VM placement problem can be designed as a genetic programming problem.The solution domain can be represented as the PM with a resource provisioning capacity. The fitness function can be defined over the number of bins in the solution. The aim would be to deliver a solution that is nearly optimal in terms of the number of bins used and the efficiency of packing of the bins.

E. Simulated Annealing Algorithm

There are certain optimization problems that become unmanageable using combinatoric methods as the number of objects becomes large. A typical example is the traveling salesman problem. While this technique is unlikely to find the optimum solution, it can often find a very good solution, even in the presence of noisy data. The VM placement problem can be designed as a simulated annealing problem. Initially a random solution is generated that could be the mapping of VMs to PMs. Then cost is calculated for this mapping after defining a cost function which could be minimum number of migrations, least power consumption or resource leakage. Then random neighbor solution is generated and its cost is calculated. Then reshuffling of VMs to PMs is done according to the minimum cost.


  • Efficient placement of the VM can improve the overall performance of the system. 
  • Virtual machine placement is a technique which maps the VM to the appropriate PM. 

  • Since the size of the data center is large in the cloud computing environment, so selecting a proper host for placing the VM is a very challenging task during the virtual machine migration. 
  • In this paper few VM placement techniques with their anomalies are presented. Each placement algorithm performs well under some specific conditions. So it is a critical task to choose a technique that is suitable for both the cloud user and cloud provider.


[1] A. Beloglazov et al., “Energy efficient allocation of virtual machines in cloud data centers”, proceeding in 10th IEEE/ACM Intl. Symp. on Cluster, Cloud and Grid Computing, pp. 577-578, may 2010.

[2] Lei Xu,Wenzhi et al., ”Smart-DRS:A Strategy of Dynamic Resource Scheduling in Cloud Data Center”, IEEE International conference on Cluster Computing Workshops, pp. 120-127, Sept..2012.

[3] N. Bobro et al., “Dynamic placement of virtual machines for managing SLA violations”, proc. in 10th IEEE international symposium on integrated network management pp. 119-128, May 2007.

[4] Yongqiang Wu, Maolin Tang and Warren Fraser. A Simulated Annealing Algorithm for Energy Efficient Virtual Machine Placement. 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea.

[5] Sosinsky, “Cloud Computing Bible”, Wiley Publishing Inc. (2012).

[6] Kim, “Experimental Study to Improve Resource Utilization and Performance of Cloud Systems based on Grid Middleware” proceeding in first International Conference on Internet (2009). 

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Public - 6/27/16, 5:41 AM