Title
Design and evaluation of learning algorithms for dynamic resource management in virtual networks Design and evaluation of learning algorithms for dynamic resource management in virtual networks
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
New york :Ieee ,
Subject
Computer. Automation
Source (journal)
2014 IEEE NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (NOMS)
Source (book)
14th IEEE/IFIP Network Operations and Management Symposium (NOMS), MAY 05-09, 2014, Krakow, POLAND
Volume/pages
(2014) , 9 p.
ISSN
1542-1201
ISBN
978-1-4799-0913-1
ISI
000356862300034
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
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