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Machine Learning for Spectrum Defragmentation in Space-Division Multiplexing Elastic Optical Networks

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Autor(es):
Trindade, Silvana ; da Fonseca, Nelson L. S.
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: IEEE NETWORK; v. 35, n. 1, p. 7-pg., 2021-01-01.
Resumo

In Elastic Optical Networks with Space Division Multiplexing, the dynamic allocation and deallocation of frequency slots can generate spectrum fragmentation, which increases the blocking of requests for lightpath establishment. In this article, we introduce a reactive algorithm and a proactive one that can jointly reduce spectrum fragmentation. We introduce a novel defragmentation approach based on an unsupervised machine learning technique to rearrange a fragmented spectrum by clustering lightpaths. A Routing, Modulation Format, Core, and Spectrum Allocation algorithm uses information on the clustering of lightpaths to establish new lightpaths for incoming requests. Results show that our approach can reduce the blocking of requests and spectrum fragmentation. (AU)

Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
Beneficiário:Nelson Luis Saldanha da Fonseca
Modalidade de apoio: Auxílio à Pesquisa - Temático