Publication
Title
Design and evaluation of learning algorithms for dynamic resource management in virtual networks
Author
Abstract
Network virtualisation is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualisation will depend in part on how efficiently the virtual networks utilise substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralised system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralised way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected.
Language
English
Source (journal)
IEEE/IFIP Network Operations and Management Symposium : [proceedings] : NOMS. - Piscataway, NJ, 2000, currens
Source (book)
14th IEEE/IFIP Network Operations and Management Symposium (NOMS), MAY 05-09, 2014, Krakow, POLAND
Publication
New york : Ieee, 2014
ISBN
978-1-4799-0913-1
Volume/pages
(2014), 9 p.
ISI
000356862300034
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.09.2015
Last edited 09.06.2017
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