Mapeamento Customizado de Serviços de Rede em Múltiplos Domínios Baseado em Heurísticas Genéticas
Resumo
A implantação de serviços de rede é um dos principais tarefas do paradigma de Virtualização de Funções de Rede (Network Function Virtualization - NFV). As soluções de mapeamento multi-domínio existentes apresentam diversas restrições em termos do modelos de otimização e métricas de avaliação que adotam. Essa inflexibilidade leva, em última instância, a mapeamentos subotimizados que não correspondem exatamente às necessidades das partes envolvidas no processo, incluindo operadores de rede, clientes, provedores, entre outros. Este trabalho propõe uma nova solução de mapeamento baseada em heurísticas genéticas. Essa nova solução permite a configuração sob demanda dos critérios de avaliação utilizados para a geração de mapeamentos candidatos, sendo capaz de considerar diferentes políticas, limitações tecnológicas e métricas de avaliação definidas de forma específica para cada requisição. Por fim, demonstramos a viabilidade da solução proposta (convergência e tempo de execução) através de um estudo de caso.
Palavras-chave:
NFV, SFC, Implantação, Mapeamento, Multi-domínio
Referências
Abujoda, A. et al. (2016). Distnse: Distributed network service embedding across multiple providers. In IEEE Int. Conf. on Communication Systems and Networks, pages 1–8.
Boubendir, A. et al. (2016). Naas architecture through sdn-enabled nfv: Network openness towards web communication service providers. In IEEE/IFIP Network Operations and Management Symposium, pages 722–726.
Cao, J. et al. (2016). Vnf placement in hybrid nfv environment: Modeling and genetic algorithms. In IEEE Int. Conf. on Parallel and Distributed Systems, pages 769–777.
Carpio, F. et al. (2017). Vnf placement with replication for load balancing in nfv networks. In IEEE Int. Conf. on Communications, pages 1–6.
Deb, K. et al. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197.
Dietrich, D. et al. (2015). Network service embedding across multiple providers with nestor. In IFIP Networking, pages 1–9.
Even, G. et al. (2019). On-line path computation and function placement in sdns. Springer Theory of Computing Systems, 63(2):306–325.
Fulber-Garcia, V. et al. (2020). CUSCO: A Customizable Solution for NFV Composition. In Int. Conf. on Advanced Information Networking and Applications, pages 204–216.
Garcia, V. F. et al. (2019). Uma solução flexı́vel e personalizável para a composição de cadeias de função de serviço. In Workshop de Gerência e Operação de Redes e Serviços, pages 99–112.
Gil-Herrera, J. et al. (2017). A scalable metaheuristic for service function chain composition. In IEEE Latin-American Conference on Communications, pages 1–6.
Handley, M. (2006). 24(3):119–129. Why the internet only just works. BT Technology Journal,
Hasançebi, O. et al. (2000). Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computers & Structures, 78(1-3):435–448.
Herrera, J. G. et al. (2016). Resource allocation in nfv: A comprehensive survey. IEEE Transactions on Network and Service Management, 13(3):518–532.
Khebbache, S. et al. (2018). A multi-objective non-dominated sorting genetic algorithm for vnf chains placement. In IEEE Annual Consumer Communications & Networking Conference, pages 1–4.
Ma, N. et al. (2017). A model based on genetic algorithm for service chain resource allocation in nfv. In IEEE Int. Conf. on Computer and Communications, pages 607– 611.
NFVISG, E. (2012). Network functions virtualization: White paper. Technical report, European Telecommunications Standards Institute.
Quinn, P. et al. (2015). Problem Statement for Service Function Chaining - RFC 7498. Technical report, Internet Engineering Task Force.
Riera, J. F. et al. (2016). Tenor: Steps towards an orchestration platform for multi-pop nfv deployment. In IEEE Conf. on Network Softwarization, pages 243–250.
Tavakoli-Someh, S. et al. (2019). Utilization-aware virtual network function placement using nsga-ii evolutionary computing. In Conf. on Knowledge Based Engineering and Innovation, pages 510–514.
Wang, Y. et al. (2017). Cost-efficient virtual network function graph (vnfg) provisioning in multidomain elastic optical networks. IEEE Journal of Lightwave Technology, 35(13):2712–2723.
Zhang, Q. et al. (2016). Vertex-centric computation of service function chains in multidomain networks. In IEEE Conf. on Network Softwarization, pages 211–218.
Zitzler, E. et al. (2001). Spea2: Improving the strength pareto evolutionary algorithm. TIK Report, 103.
Boubendir, A. et al. (2016). Naas architecture through sdn-enabled nfv: Network openness towards web communication service providers. In IEEE/IFIP Network Operations and Management Symposium, pages 722–726.
Cao, J. et al. (2016). Vnf placement in hybrid nfv environment: Modeling and genetic algorithms. In IEEE Int. Conf. on Parallel and Distributed Systems, pages 769–777.
Carpio, F. et al. (2017). Vnf placement with replication for load balancing in nfv networks. In IEEE Int. Conf. on Communications, pages 1–6.
Deb, K. et al. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197.
Dietrich, D. et al. (2015). Network service embedding across multiple providers with nestor. In IFIP Networking, pages 1–9.
Even, G. et al. (2019). On-line path computation and function placement in sdns. Springer Theory of Computing Systems, 63(2):306–325.
Fulber-Garcia, V. et al. (2020). CUSCO: A Customizable Solution for NFV Composition. In Int. Conf. on Advanced Information Networking and Applications, pages 204–216.
Garcia, V. F. et al. (2019). Uma solução flexı́vel e personalizável para a composição de cadeias de função de serviço. In Workshop de Gerência e Operação de Redes e Serviços, pages 99–112.
Gil-Herrera, J. et al. (2017). A scalable metaheuristic for service function chain composition. In IEEE Latin-American Conference on Communications, pages 1–6.
Handley, M. (2006). 24(3):119–129. Why the internet only just works. BT Technology Journal,
Hasançebi, O. et al. (2000). Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computers & Structures, 78(1-3):435–448.
Herrera, J. G. et al. (2016). Resource allocation in nfv: A comprehensive survey. IEEE Transactions on Network and Service Management, 13(3):518–532.
Khebbache, S. et al. (2018). A multi-objective non-dominated sorting genetic algorithm for vnf chains placement. In IEEE Annual Consumer Communications & Networking Conference, pages 1–4.
Ma, N. et al. (2017). A model based on genetic algorithm for service chain resource allocation in nfv. In IEEE Int. Conf. on Computer and Communications, pages 607– 611.
NFVISG, E. (2012). Network functions virtualization: White paper. Technical report, European Telecommunications Standards Institute.
Quinn, P. et al. (2015). Problem Statement for Service Function Chaining - RFC 7498. Technical report, Internet Engineering Task Force.
Riera, J. F. et al. (2016). Tenor: Steps towards an orchestration platform for multi-pop nfv deployment. In IEEE Conf. on Network Softwarization, pages 243–250.
Tavakoli-Someh, S. et al. (2019). Utilization-aware virtual network function placement using nsga-ii evolutionary computing. In Conf. on Knowledge Based Engineering and Innovation, pages 510–514.
Wang, Y. et al. (2017). Cost-efficient virtual network function graph (vnfg) provisioning in multidomain elastic optical networks. IEEE Journal of Lightwave Technology, 35(13):2712–2723.
Zhang, Q. et al. (2016). Vertex-centric computation of service function chains in multidomain networks. In IEEE Conf. on Network Softwarization, pages 211–218.
Zitzler, E. et al. (2001). Spea2: Improving the strength pareto evolutionary algorithm. TIK Report, 103.
Publicado
07/12/2020
Como Citar
FÜLBER-GARCIA, Vinícius; DOS SANTOS, Carlos Raniery Paula ; SPINOSA, Eduardo Jaques; DUARTE JR., Elias Procópio.
Mapeamento Customizado de Serviços de Rede em Múltiplos Domínios Baseado em Heurísticas Genéticas. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 491-504.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12304.
