Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://ruomo.lib.uom.gr/handle/7000/313
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Liberati, Francesco | - |
dc.contributor.author | Giuseppi, Alessandro | - |
dc.contributor.author | Pietrabissa, Antonio | - |
dc.contributor.author | Suraci, Vincenzo | - |
dc.contributor.author | Di Giorgio, Alessandro | - |
dc.contributor.author | Trubian, Marco | - |
dc.contributor.author | Dietrich, David | - |
dc.contributor.author | Papadimitriou, Panagiotis | - |
dc.contributor.author | Delli Priscoli, Francesco | - |
dc.date.accessioned | 2019-10-30T10:14:46Z | - |
dc.date.available | 2019-10-30T10:14:46Z | - |
dc.date.issued | 2017-11 | - |
dc.identifier | 10.1002/nem.1985 | en_US |
dc.identifier.issn | 10557148 | en_US |
dc.identifier.uri | https://doi.org/10.1002/nem.1985 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/313 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.source | International Journal of Network Management | en_US |
dc.subject | FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | en_US |
dc.subject.other | Network Function Virtualization | en_US |
dc.subject.other | Resource Allocation | en_US |
dc.subject.other | Reinforcement learning | en_US |
dc.subject.other | Linear programming | en_US |
dc.title | Stochastic and exact methods for service mapping in virtualized network infrastructures | en_US |
dc.type | Article | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.volume | 27 | en_US |
local.identifier.issue | 6 | en_US |
local.identifier.firstpage | 1 | en_US |
local.identifier.lastpage | 19 | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
---|---|---|---|---|
wiley.pdf | 833,24 kB | Adobe PDF | Προβολή/Ανοιγμα |
Τα τεκμήρια στο Αποθετήριο προστατεύονται από πνευματικά δικαιώματα, εκτός αν αναφέρεται κάτι διαφορετικό.