Publicação
Evolutionary computation for quality of service internet routing optimization
| Resumo: | In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Service levels 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 Computation arena 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 Evolution and some common heuristic methods including local search were compared. EAs make the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios. |
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| Autores principais: | Rocha, Miguel |
| Outros Autores: | Sousa, Pedro; Cortez, Paulo; Rio, Miguel |
| Assunto: | Traffic engineering Quality of service routing Evolutionary algorithms Differential evolution OSPF |
| Ano: | 2007 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Service levels 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 Computation arena 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 Evolution and some common heuristic methods including local search were compared. EAs make the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios. |
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