An inhomogeneous Hidden Markov model for efficient virtual machine placement in a cloud computing enviroment


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In a cloud environment virtual machines are created with different purposes like providing users with computers, handling web traffic etc. A virtual machine is created in such a way that a user will not notice any differences from working on a physical computer. A challenging problem in cloud computing is how to distribute the virtual machines on a set of physical servers. An optimal solution will provide each virtual machine with enough resources and at the same time not using more physical serves (energy/electricity) than necessary to achieve this. In this paper we investigate how forecasting of future resource re- quirements (CPU conspumption) for each virtual machine can be used to improve the virtual machine placement on the physical servers. We demonstrate that a time dependent Hidden Markov model with an autoregressive observation process replicates the properties the CPU consumption data in a realistic way and forecasts future CPU con- sumption efficiently.




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