@CONFERENCE{Pietrabissa20159084,
author={Pietrabissa, A. and Battilotti, S. and Facchinei, F. and Giuseppi, A. and Oddi, G. and Panfili, M. and Suraci, V.},
title={Resource management in multi-cloud scenarios via reinforcement learning},
journal={Chinese Control Conference, CCC},
year={2015},
volume={2015-September},
pages={9084-9089},
doi={10.1109/ChiCC.2015.7261077},
art_number={7261077},
abstract={The concept of Virtualization of Network Resources, such as cloud storage and computing power, has become crucial to any business that needs dynamic IT resources. With virtualization, we refer to the migration of various tasks, usually performed by hardware infrastructures, to virtual IT resources. This approach allows resources to be rapidly deployed, scaled and dynamically reassigned. In the last few years, the demand of cloud resources has grown dramatically, and a new figure plays a key role: the Cloud Management Broker (CMB). The CMB purpose is to manage cloud resources to meet the user's requirements and, at the same time, to optimize their usage. This paper proposes two multi-cloud resource allocation algorithms that manage the resource requests with the aim of maximizing the CMB revenue over time. The algorithms, based on Reinforcement Learning techniques, are evaluated and compared by numerical simulations. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.},
author_keywords={Cloud networks;  Markov Decision Process;  Reinforcement Learning;  Resource Management},
document_type={Conference Paper},
}

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