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New MPLS network management techniques based on adaptive learning
Journal article

New MPLS network management techniques based on adaptive learning

Tricha Anjali, Caterina Scoglio and Jaudelice Cavalcante de Oliveira
IEEE transactions on neural networks, v 16(5), pp 1242-1255
Sep 2005
DOI: https://doi.org/10.1109/TNN.2005.853428
PMID: 16252830

Abstract

Algorithms Information Storage and Retrieval - methods Artificial Intelligence Computer Simulation Signal Processing, Computer-Assisted Internet Models, Statistical Telecommunications Pattern Recognition, Automated - methods
The combined use of the differentiated services (DiffServ) and multiprotocol label switching (MPLS) technologies is envisioned to provide guaranteed quality of service (QoS) for multimedia traffic in IP networks, while effectively using network resources. These networks need to be managed adaptively to cope with the changing network conditions and provide satisfactory QoS. An efficient strategy is to map the traffic from different DiffServ classes of service on separate label switched paths (LSPs), which leads to distinct layers of MPLS networks corresponding to each DiffServ class. In this paper, three aspects of the management of such a layered MPLS network are discussed. In particular, an optimal technique for the setup of LSPs, capacity allocation of the LSPs and LSP routing are presented. The presented techniques are based on measurement of the network state to adapt the network configuration to changing traffic conditions.

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4 citations in Web of Science
7 citations in Scopus

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Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
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