@misc{10481/96472,
year = {2024},
month = {8},
url = {https://hdl.handle.net/10481/96472},
abstract = {The recent boom of 5G networks, coupled with the escalating demand among users for higher-capacity connections, is presenting a formidable challenge. 5G networks encompass a diverse array of advantageous technologies, prominently including Software- Defined Networking (SDN) together with Network Function Virtualization (NFV), in which a key problem is the optimal development of Service Function Chaining (SFC). This paper presents two approaches based on Genetic Algorithms (GAs), for the optimal composition of SFCs focused on network pathways, and tailored for the service chain required in different 5G connections (user service requests). The approaches have been tested on different instances of the problem with topologies including 6, 19 and 52 nodes. The obtained results are promising, showing optimal paths inside simple and complex topologies for every connection as well as for multiple connections.},
organization = {MICIU/AEI/10.13039/501100011033 TED2021-131699B-I00, TED2021-129938B-I00},
organization = {European Union NextGenerationEU/PRTR},
organization = {Spanish Ministry of Economy and Competitiveness PID2020-113462RB-I00, PID2020-115570GB-C22},
organization = {Andalusian Government, FEDER Program 2021-2027 C-ING-179-UGR23},
publisher = {IEEE},
keywords = {5G},
keywords = {Software-Defined Networks},
keywords = {Genetic Algorithm},
keywords = {Service Function Chaining},
keywords = {Route Optimization},
title = {Applying Evolutionary Algorithms for Service Function Chaining in 5G Networks},
author = {Mora García, Antonio Miguel and Victoria-Mohammed, J. and Medina Medina, Nuria and Valenzuela Valdes, Juan Francisco},
}