An Inhomogeneous Hidden Markov Model for Efficient Virtual Machine Placement in Cloud Computing Environments
Hugo L. Hammer, Anis Yazidi, Kyrre Begnum · Journal of Forecasting · 2016
In a cloud environment virtual machines are created with different purposes, such as providing users with computers or handling web traffic. A virtual machine is created in such a way that a user will not notice any difference 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 services (energy/electricity) than necessary to achieve this. In this paper we investigate how forecasting of future resource requirements (CPU consumption) 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 of the CPU consumption data in a realistic way and forecasts future CPU consumption efficiently. Copyright © 2016 John Wiley & Sons, Ltd.