@ARTICLE{Liberati2017,
author={Liberati, F. and Giuseppi, A. and Pietrabissa, A. and Suraci, V. and Di Giorgio, A. and Trubian, M. and Dietrich, D. and Papadimitriou, P. and Delli Priscoli, F.},
title={Stochastic and exact methods for service mapping in virtualized network infrastructures},
journal={International Journal of Network Management},
year={2017},
volume={27},
number={6},
doi={10.1002/nem.1985},
art_number={e1985},
scopus={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026350530&doi=10.1002%2fnem.1985&partnerID=40&md5=b2d3a7eaa40c6ca1251b03830eaa09df},
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.},
keywords={Conformal mapping;  Decision theory;  Heuristic methods;  Integer programming;  Markov processes;  Reinforcement learning;  Stochastic systems, Integer Linear Programming;  Mapping algorithms;  Markov Decision Processes;  Mathematical formulation;  Network infrastructure;  Numeric simulation;  Stochastic algorithms;  Virtual network services, Iterative methods},
document_type={Article},
source={Scopus},
}

{"_id":"zHMcJeNMX6Szte2g2","bibbaseid":"liberati-giuseppi-pietrabissa-suraci-digiorgio-trubian-dietrich-papadimitriou-etal-stochasticandexactmethodsforservicemappinginvirtualizednetworkinfrastructures-2017","authorIDs":["cnSbkZ6mcMEB5wsJn"],"author_short":["Liberati, F.","Giuseppi, A.","Pietrabissa, A.","Suraci, V.","Di Giorgio, A.","Trubian, M.","Dietrich, D.","Papadimitriou, P.","Delli Priscoli, F."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Liberati"],"firstnames":["F."],"suffixes":[]},{"propositions":[],"lastnames":["Giuseppi"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Pietrabissa"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Suraci"],"firstnames":["V."],"suffixes":[]},{"propositions":[],"lastnames":["Di Giorgio"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Trubian"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Dietrich"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Papadimitriou"],"firstnames":["P."],"suffixes":[]},{"propositions":[],"lastnames":["Delli Priscoli"],"firstnames":["F."],"suffixes":[]}],"title":"Stochastic and exact methods for service mapping in virtualized network infrastructures","journal":"International Journal of Network Management","year":"2017","volume":"27","number":"6","doi":"10.1002/nem.1985","art_number":"e1985","scopus":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026350530&doi=10.1002%2fnem.1985&partnerID=40&md5=b2d3a7eaa40c6ca1251b03830eaa09df","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.","keywords":"Conformal mapping; Decision theory; Heuristic methods; Integer programming; Markov processes; Reinforcement learning; Stochastic systems, Integer Linear Programming; Mapping algorithms; Markov Decision Processes; Mathematical formulation; Network infrastructure; Numeric simulation; Stochastic algorithms; Virtual network services, Iterative methods","document_type":"Article","source":"Scopus","bibtex":"@ARTICLE{Liberati2017,\nauthor={Liberati, F. and Giuseppi, A. and Pietrabissa, A. and Suraci, V. and Di Giorgio, A. and Trubian, M. and Dietrich, D. and Papadimitriou, P. and Delli Priscoli, F.},\ntitle={Stochastic and exact methods for service mapping in virtualized network infrastructures},\njournal={International Journal of Network Management},\nyear={2017},\nvolume={27},\nnumber={6},\ndoi={10.1002/nem.1985},\nart_number={e1985},\nscopus={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026350530&doi=10.1002%2fnem.1985&partnerID=40&md5=b2d3a7eaa40c6ca1251b03830eaa09df},\nabstract={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.},\nkeywords={Conformal mapping; Decision theory; Heuristic methods; Integer programming; Markov processes; Reinforcement learning; Stochastic systems, Integer Linear Programming; Mapping algorithms; Markov Decision Processes; Mathematical formulation; Network infrastructure; Numeric simulation; Stochastic algorithms; Virtual network services, Iterative methods},\ndocument_type={Article},\nsource={Scopus},\n}\n\n","author_short":["Liberati, F.","Giuseppi, A.","Pietrabissa, A.","Suraci, V.","Di Giorgio, A.","Trubian, M.","Dietrich, D.","Papadimitriou, P.","Delli Priscoli, F."],"key":"Liberati2017","id":"Liberati2017","bibbaseid":"liberati-giuseppi-pietrabissa-suraci-digiorgio-trubian-dietrich-papadimitriou-etal-stochasticandexactmethodsforservicemappinginvirtualizednetworkinfrastructures-2017","role":"author","urls":{},"keyword":["Conformal mapping; Decision theory; Heuristic methods; Integer programming; Markov processes; Reinforcement learning; Stochastic systems","Integer Linear Programming; Mapping algorithms; Markov Decision Processes; Mathematical formulation; Network infrastructure; Numeric simulation; Stochastic algorithms; Virtual network services","Iterative methods"],"metadata":{"authorlinks":{"giuseppi, a":"https://967274320-atari-embeds.googleusercontent.com/embeds/16cb204cf3a9d4d223a0a3fd8b0eec5d/inner-frame-minified.html"}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/network/files/jcGsyMGciNhktiGk3","creationDate":"2021-02-05T20:52:40.146Z","downloads":0,"keywords":["conformal mapping; decision theory; heuristic methods; integer programming; markov processes; reinforcement learning; stochastic systems","integer linear programming; mapping algorithms; markov decision processes; mathematical formulation; network infrastructure; numeric simulation; stochastic algorithms; virtual network services","iterative methods"],"search_terms":["stochastic","exact","methods","service","mapping","virtualized","network","infrastructures","liberati","giuseppi","pietrabissa","suraci","di giorgio","trubian","dietrich","papadimitriou","delli priscoli"],"title":"Stochastic and exact methods for service mapping in virtualized network infrastructures","year":2017,"dataSources":["LnFruMKQos7repGrF","pNu9oaGWEHH7o22SP","5EzEYy34nHSRYHdGQ","gfA6sQjmaq9WsWFdK","iGZjjyKe2m2P6Gg4W","ZZdxNsgmnZu3pHpvP"]}