Abstract

Part 1: Traffic Engineering and Quality-of-Service; International audience; In current network infrastructures, several management tasks often require significant human intervention and can be of high complexity, having to consider several inputs to attain efficient configurations. In this perspective, this work presents an optimization framework able to automatically provide network administrators with efficient and robust routing configurations. The proposed optimization tool resorts to techniques from the field of Evolutionary Computation, where Evolutionary Algorithms (EAs) are used as optimization engines to solve the envisaged NP-hard problems. The devised methods focus on versatile and resilient aware Traffic Engineering (TE) approaches, which are integrated into an autonomous optimization framework able to assist network administrators. Some examples of the supported TE optimization methods are presented, including preventive, reactive and multi-topology solutions, taking advantage of the EAs optimization capabilities.

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http://dx.doi.org/10.1007/978-3-642-38998-6_1 under the license cc-by
https://hal.inria.fr/hal-01489965/document,
https://hal.inria.fr/hal-01489965/file/978-3-642-38998-6_1_Chapter.pdf
https://hal.inria.fr/hal-01489965/document,
https://www.scipedia.com/public/Pereira_et_al_2013a,
http://repositorium.sdum.uminho.pt/handle/1822/26708,
http://discovery.ucl.ac.uk/1448985,
https://dblp.uni-trier.de/db/conf/aims/aims2013.html#PereiraRCRS13,
https://rd.springer.com/chapter/10.1007/978-3-642-38998-6_1,
https://hal.archives-ouvertes.fr/hal-01489965v1,
https://hal.inria.fr/IFIP-LNCS-7943/hal-01489965,
https://academic.microsoft.com/#/detail/1532873320 under the license http://creativecommons.org/licenses/by/
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Published on 01/01/2013

Volume 2013, 2013
DOI: 10.1007/978-3-642-38998-6_1
Licence: CC BY-NC-SA license

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