Evolutionary Computation for Quality of Service Internet Routing Optimization

Authors:
Miguel Rocha;Pedro Sousa;Paulo Cortez;Miguel Rio
Affiliations:
Dep. Informatics / CCTC - Univ. Minho - Braga, Portugal;Dep. Informatics / CCTC - Univ. Minho - Braga, Portugal;Dep. Information Systems/ Algoritmi - Univ. Minho - Guimarães, Portugal;University College London - London, UK
Venue:
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Year:
2009

Citing 8
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Internet QoS: Architectures and Mechanisms for Quality of Service

Internet QoS: Architectures and Mechanisms for Quality of Service
OSPF Network Design Solutions

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OSPF: Anatomy of an Internet Routing Protocol
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Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Towards a new internet architecture
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BRITE: Universal Topology Generation from a User''s Perspective
A hybrid genetic algorithm for the weight setting problem in OSPF-IS-IS routing

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Abstract

In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Servicelevels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a complex problem, some meta-heuristics from the Evolutionary Computationarena were considered, working over a mathematical model that allows for flexible cost functions, taking into account several measures of the network behavior such as network congestion and end-to-end delays. A number of experiments were performed, resorting to a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolutionand some common heuristic methods including local searchwere compared. EAsmake the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios.