Tronco: Um Operador Genético para Remapeamento de Serviços de Rede Sensível ao Histórico
Resumo
Uma das principais vantagens do paradigma da Virtualização de Funções de Rede (NFV) é a flexibilidade trazida para o núcleo da rede. Entretanto, é notável a lacuna de estratégias para o mapeamento de serviços de rede baseados em NFV que levam em conta que as redes de comunicações são dinâmicas, no sentido de que seu estado se altera gradualmente com o tempo. Por isso, este artigo apresenta um novo operador genético para o mapeamento dinâmico de serviços virtualizados em diferentes domínios e pontos de presença. Apesar de soluções de mapeamento baseadas em algoritmos genéticos apresentarem bons resultados para o processo de mapeamento inicial, elas desconsideram informações históricas para o remapeamento do serviço quando o estado da rede muda. O operador aqui proposto, denominado Tronco, utiliza dados históricos para garantir a otimalidade (local ou global) do remapeamento de serviços de rede. Os resultados experimentais de sua implementação são apresentados para diversos cenários, considerando: (i) o mesmo número de gerações; (ii) a execução até a convergência; e (iii) diferentes níveis de mudança no estado da rede. Os resultados demonstram melhorias significativas no remapeamento de serviços virtualizados com o uso do Tronco.Referências
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De Falco, I. et al. (2002). Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4):285–299.
Forrest, S. (1996). Genetic algorithms. ACM Computing Surveys, 28(1):77–80.
Fulber-Garcia, V. et al. (2020). Network service topology: Formalization, taxonomy and the custom specification model. Computer Networks, 178:107337.
Fulber-Garcia, V. et al. (2023). Customizable mapping of virtualized network services in multi-datacenter environments based on genetic metaheuristics. Journal of Network and Systems Management, 31(4):71.
Fulber-Garcia, V. et al. (2024a). Breaking the limits: Bio-inspired sfc deployment across multiple domains, clouds and orchestrators. In IEEE Conf. on Network Function Virtualization and Software Defined Networks, pages 1–6.
Fulber-Garcia, V. et al. (2024b). Uma estratégia bioinspirada para alocação dinâmica de sfcs em múltiplos domínios, nuvens e orquestradores nfv. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 183–196.
Han, S. et al. (2020). Virtualization in programmable data plane: A survey and open challenges. IEEE Open Journal of the Communications Society, 1:527–534.
Herrera, J. G. and Botero, J. F. (2016). Resource allocation in nfv: A comprehensive survey. IEEE Trans. on Network and Service Management, 13(3):518–532.
Huff, A. et al. (2020). Building multi-domain service function chains based on multiple nfv orchestrators. In IEEE Conf. on Network Function Virtualization and Software Defined Networks, pages 19–24.
Lambora, A. et al. (2019). Genetic algorithm-a literature review. In Int. Conf. on Machine Learning, Big Data, Cloud and Parallel Computing, pages 380–384.
Li, Y. et al. (2020). Cost-and-qos-based nfv service function chain mapping mechanism. In IEEE/IFIP Network Operations and Management Symposium, pages 1–9.
Liu, J. et al. (2024). Multi-objective model and genetic algorithm for multisource multi-cast vnf service chain deployment problem. Int. Journal of Computer Science, 51(6).
Oliveira, G. W. et al. (2026). Observability: The missing piece of management in nfv-based network environments. In International Workshop on ADVANCEs in ICT Infrastructures and Services.
Pachuau, J. L. et al. (2021). An overview of crossover techniques in genetic algorithm. Modeling, Simulation and Optimization, pages 581–598.
Rankothge, W. et al. (2015). Experimental results on the use of genetic algorithms for scaling virtualized network functions. In Conf. on Network Function Virtualization and Software Defined Network, pages 47–53.
Rehman, A. et al. (2019). Network functions virtualization: The long road to commercial deployments. IEEE Access, 7:60439–60464.
Rodis, P. and Papadimitriou, P. (2021). Intelligent network service embedding using genetic algorithms. In IEEE Symp. on Computers and Communications, pages 1–7.
Shukla, A. et al. (2015). Comparative review of selection techniques in genetic algorithm. In Int. Conf. on Futuristic Trends on Computational Analysis and Knowledge Management, pages 515–519.
Souza, R. et al. (2020). Nfv data centers: A systematic review. IEEE Access, 8:51713–51735.
Voorneveld, M. (2003). Characterization of pareto dominance. Operations Research Letters, 31(1):7–11.
Ward, P. S. (2010). Taxonomy, phylogenetics, and evolution. Ant ecology, 1.
Zhang, T. et al. (2020). Nfv platforms: Taxonomy, design choices and future challenges. IEEE Trans. on Network and Service Management, 18(1):30–48.
Publicado
25/05/2026
Como Citar
OLIVEIRA, Guilherme Werneck de; DUARTE JR., Elias P.; FULBER-GARCIA, Vinícius.
Tronco: Um Operador Genético para Remapeamento de Serviços de Rede Sensível ao Histórico. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 44. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1345-1358.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2026.19212.
