@ARTICLE{Pietrabissa2017508,
author={Pietrabissa, A. and Priscoli, F.D. and Di Giorgio, A. and Giuseppi, A. and Panfili, M. and Suraci, V.},
title={An approximate dynamic programming approach to resource management in multi-cloud scenarios},
journal={International Journal of Control},
year={2017},
volume={90},
number={3},
pages={508-519},
doi={10.1080/00207179.2016.1185802},
abstract={The programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers’ requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={approximate dynamic programming;  Cloud networks;  Markov decision process;  reinforcement learning;  resource management},
keywords={Distributed computer systems;  Economics;  Markov processes;  Natural resources management;  Reinforcement learning;  Resource allocation, Approximate dynamic programming;  Approximate solution;  Cloud networks;  Information and communications technology;  Markov Decision Processes;  Reinforcement learning techniques;  Resource allocation algorithms;  Resource management, Dynamic programming},
document_type={Article},
}

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