Genetic Heuristic-based Customizable Mapping of Network Services in Multiple Domains
Abstract
The deployment of network services is one of the major tasks of the Network Function Virtualization (NFV) paradigm. However, current multi-domain mapping solutions present several constraints regarding the adopted optimization models and evaluation metrics. This inflexibility ultimately leads to sub-optimized mappings that do not meet the requirements of the parties involved in the process (e.g., network operators, clients, providers, etc.). This work proposes a new mapping solution based on genetic heuristics. The new solution enables the configuration on-demand of the evaluation criteria used to generate candidate mappings. The solution takes into consideration different policies, technological constraints, and evaluation metrics which are specified individually, for each request document. Finally, we demonstrate the feasibility of the proposed solution (convergence and execution time) through a case study.
Keywords:
NFV, SFC, Deployment, Mapping, Multi-domain
References
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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.
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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.
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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.
Published
2020-12-07
How to Cite
FÜLBER-GARCIA, Vinícius; DOS SANTOS, Carlos Raniery Paula ; SPINOSA, Eduardo Jaques; DUARTE JR., Elias Procópio.
Genetic Heuristic-based Customizable Mapping of Network Services in Multiple Domains. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (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.
