Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/370
Title: Stochastic and exact methods for service mapping in virtualized network infrastructures
Authors: Liberati, Francesco
Giuseppi, Alessandro
Pietrabissa, Antonio
Suraci, Vincenzo
Di Giorgio, Alessandro
Trubian, Marco
Dietrich, David
Papadimitriou, Panagiotis
Delli Priscoli, Francesco
Subjects: FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Issue Date: 2017
Source: International Journal of Network Management
Volume: 27
Issue: 6
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.
URI: https://doi.org/10.1002/nem.1985
https://ruomo.lib.uom.gr/handle/7000/370
ISSN: 10557148
Other Identifiers: 10.1002/nem.1985
Appears in Collections:Department of Applied Informatics

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