Reinforcement learning based autonomic virtual machine management in clouds
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Date
11/28/2016
Authors
Habib, Arafat
Khan, Muhidulislam
Journal Title
Journal ISSN
Volume Title
Publisher
© 2016 IEEE
Abstract
Cloud computing is a rapidly emerging field, services and applications are more or less 24/7. Resource dimensioning in this field is a great issue. Research is already going on to imply reinforcement learning to automate decision making process in case of addition, reduction, migration and maintenance of the Virtual Machines (VM) to balance the service level performance and VM management cost. Models have been proposed in this case based on Q-learning, a very popular reinforcement learning technique that is used to find optimal action selection policy for any finite Markov Decision Process (MDP). In this paper we propose to work with the challenges like proper initialization of the early stages, designing the states, actions, transitions using Markov Decision Process (MDP) and solving the MDP with two popular reinforcement learning techniques, Q-learning and SARSA(Λ).
Description
This conference paper was published in the IEEE Xplore [ © 2016 IEEE] and the definite version is available at : http://doi.org/10.1109/ICIEV.2016.7760166 The Journal's website is at: http://ieeexplore.ieee.org/document/7760166/
Keywords
Cloud computing, Markov decision process, Reinforcement learning, Virtual machines
Citation
Habib, A., & Khan, M. I. (2016). Reinforcement learning based autonomic virtual machine management in clouds. Paper presented at the 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, 1083-1088. 10.1109/ICIEV.2016.7760166