| Autor: |
Enrique Dávalos, José-Luis Enciso, Nicolás Silva, Juan Pinto-Ríos, Ariel Leiva |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
ICT Express, Vol 9, Iss 5, Pp 890-895 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9595 |
| DOI: |
10.1016/j.icte.2023.01.008 |
| Popis: |
Bandwidth fragmentation is a critical problem for Elastic Optical Networks (EON), and spectrum defragmentation is the most important strategy to mitigate this phenomenon. In this work we propose a Machine Learning (ML) based method for estimating the Blocking Rate, which, when exceeding a threshold, triggers a defragmentation process. This is done in order to achieve better results in terms of the number of blocking demands and the number of re-routed connections. The performance of the proposed method was compared with two other known strategies: fixed-time (FT) defragmentation, and triggering based on one fragmentation metric (BFR). Simulation results were evaluated using two multi-objective metrics. Experimental results show that the proposed method is more efficient than the other two, being the best method in 85.7% of comparisons using the Pareto Coverage metric, and obtaining 47.4% of non-dominated solutions in the Pareto Front. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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