This paper presents a stochastic algorithm for virtual network service mapping in virtualized network infrastructures, based on reinforcement learning (RL). An exact mapping algorithm in line with the current state of the art and based on integer linear programming is proposed as well, and the performances of the two algorithms are compared. While most of the current works in literature report exact or heuristic mapping methods, the RL algorithm presented here is instead a stochastic one, based on Markov decision processes theory. The aim of the RL algorithm is to iteratively learn an efficient mapping policy, which could maximize the expected mapping reward in the long run. Based on the review of the state of the art, the paper presents a general model of the service mapping problem and the mathematical formulation of the 2 proposed strategies. The distinctive features of the 2 algorithms, their strengths, and possible drawbacks are discussed and validated by means of numeric simulations in a realistic emulated environment. Copyright © 2017 John Wiley & Sons, Ltd.

Stochastic and exact methods for service mapping in virtualized network infrastructures / Liberati, Francesco; Giuseppi, Alessandro; Pietrabissa, Antonio; Suraci, Vincenzo; DI GIORGIO, Alessandro; Trubian, Marco; Dietrich, David; Papadimitriou, Panagiotis; DELLI PRISCOLI, Francesco. - In: INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT. - ISSN 1055-7148. - STAMPA. - 27:6(2017), pp. 1-19. [10.1002/nem.1985]

Stochastic and exact methods for service mapping in virtualized network infrastructures

Liberati, Francesco
;
GIUSEPPI, ALESSANDRO;PIETRABISSA, Antonio;DI GIORGIO, ALESSANDRO;DELLI PRISCOLI, Francesco
2017

Abstract

This paper presents a stochastic algorithm for virtual network service mapping in virtualized network infrastructures, based on reinforcement learning (RL). An exact mapping algorithm in line with the current state of the art and based on integer linear programming is proposed as well, and the performances of the two algorithms are compared. While most of the current works in literature report exact or heuristic mapping methods, the RL algorithm presented here is instead a stochastic one, based on Markov decision processes theory. The aim of the RL algorithm is to iteratively learn an efficient mapping policy, which could maximize the expected mapping reward in the long run. Based on the review of the state of the art, the paper presents a general model of the service mapping problem and the mathematical formulation of the 2 proposed strategies. The distinctive features of the 2 algorithms, their strengths, and possible drawbacks are discussed and validated by means of numeric simulations in a realistic emulated environment. Copyright © 2017 John Wiley & Sons, Ltd.
2017
Computer Science Applications1707; Computer Vision and Pattern Recognition; Computer Networks and Communications
01 Pubblicazione su rivista::01a Articolo in rivista
Stochastic and exact methods for service mapping in virtualized network infrastructures / Liberati, Francesco; Giuseppi, Alessandro; Pietrabissa, Antonio; Suraci, Vincenzo; DI GIORGIO, Alessandro; Trubian, Marco; Dietrich, David; Papadimitriou, Panagiotis; DELLI PRISCOLI, Francesco. - In: INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT. - ISSN 1055-7148. - STAMPA. - 27:6(2017), pp. 1-19. [10.1002/nem.1985]
File allegati a questo prodotto
File Dimensione Formato  
Liberati_Stochastic-and-exact_2017.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.46 MB
Formato Adobe PDF
2.46 MB 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/11573/1002156
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 11
social impact