The Journal of Supercomputing

, Volume 70, Issue 3, pp 1279–1296

Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment


DOI: 10.1007/s11227-014-1227-5

Cite this article as:
Tang, Z., Mo, Y., Li, K. et al. J Supercomput (2014) 70: 1279. doi:10.1007/s11227-014-1227-5


In most cloud computing platforms, the virtual machine quotas are seldom changed once initialized, although the current allocated resources are not efficiently utilized. The average utilization of cloud servers in most datacenters can be improved through virtual machine placement optimization. How to dynamically forecast the resource usage becomes a key problem. This paper proposes a scheduling algorithm called virtual machine dynamic forecast scheduling (VM-DFS) to deploy virtual machines in a cloud computing environment. In this algorithm, through analysis of historical memory consumption, the most suitable physical machine can be selected to place a virtual machine according to future consumption forecast. This paper formalizes the virtual machine placement problem as a bin-packing problem, which can be solved by the first-fit decreasing scheme. Through this method, for specific virtual machine requirements of applications, we can minimize the number of physical machines. The VM-DFS algorithm is verified through the CloudSim simulator. Our experiments are carried out on different numbers of virtual machine requests. Through analysis of the experimental results, we find that VM-DFS can save 17.08 % physical machines on the average, which outperforms most of the state-of-the-art systems.


Bin packing Dynamic scheduling Forecast Virtualization  Virtual machine placement 

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHunan UniversityChangshaChina
  2. 2.Department of Computer ScienceState University of New YorkNew PaltzUSA

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