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.

An approximate dynamic programming approach to resource management in multi-cloud scenarios / Pietrabissa, Antonio; Delli Priscoli, Francesco; Di Giorgio, Alessandro; Giuseppi, Alessandro; Panfili, Martina; Suraci, Vincenzo. - In: INTERNATIONAL JOURNAL OF CONTROL. - ISSN 0020-7179. - STAMPA. - 90:3(2017), pp. 492-503. [10.1080/00207179.2016.1185802]

An approximate dynamic programming approach to resource management in multi-cloud scenarios

PIETRABISSA, Antonio
;
DELLI PRISCOLI, Francesco;DI GIORGIO, ALESSANDRO;Giuseppi, Alessandro;PANFILI, MARTINA;
2017

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.
2017
approximate dynamic programming; Cloud networks; Markov decision process; reinforcement learning; resource management; Control and Systems Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition
01 Pubblicazione su rivista::01a Articolo in rivista
An approximate dynamic programming approach to resource management in multi-cloud scenarios / Pietrabissa, Antonio; Delli Priscoli, Francesco; Di Giorgio, Alessandro; Giuseppi, Alessandro; Panfili, Martina; Suraci, Vincenzo. - In: INTERNATIONAL JOURNAL OF CONTROL. - ISSN 0020-7179. - STAMPA. - 90:3(2017), pp. 492-503. [10.1080/00207179.2016.1185802]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/898050
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