Abstract
This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.
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Notes
- 1.
When a solution is dominated by another one, it means that it is worse than the second in at least one of the objectives and it is not better in none.
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Acknowledgments
This work has been partially supported by FCT - Fundação para a Ciência e Tecnologia Portugal in the scope of the project: UID/CEC/00319/2013.
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Pereira, V., Rocha, M., Sousa, P. (2016). Automated Network Resilience Optimization Using Computational Intelligence Methods. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_46
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