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.

Resource management in multi-cloud scenarios via reinforcement learning / Pietrabissa, Antonio; Battilotti, Stefano; Facchinei, Francisco; Giuseppi, Alessandro; Oddi, Guido; Panfili, Martina; Suraci, Vincenzo. - STAMPA. - (2015), pp. 9084-9089. (Intervento presentato al convegno Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, China tenutosi a Hangzhou; China) [10.1109/ChiCC.2015.7261077].

Resource management in multi-cloud scenarios via reinforcement learning

PIETRABISSA, Antonio
;
BATTILOTTI, Stefano
;
FACCHINEI, Francisco;Giuseppi, Alessandro;PANFILI, MARTINA;
2015

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
Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, China
Cloud networks; Markov Decision Process; Reinforcement Learning; Resource Management; Computer Science Applications1707 Computer Vision and Pattern Recognition; Control and Systems Engineering; Applied Mathematics; Modeling and Simulation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Resource management in multi-cloud scenarios via reinforcement learning / Pietrabissa, Antonio; Battilotti, Stefano; Facchinei, Francisco; Giuseppi, Alessandro; Oddi, Guido; Panfili, Martina; Suraci, Vincenzo. - STAMPA. - (2015), pp. 9084-9089. (Intervento presentato al convegno Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, China tenutosi a Hangzhou; China) [10.1109/ChiCC.2015.7261077].
File allegati a questo prodotto
File Dimensione Formato  
Pietrabissa_Resource-managemen_2015.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 373.81 kB
Formato Adobe PDF
373.81 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/911565
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 4
social impact