Abstract
We investigate the problem of optimally placing virtual network functions in 5G-based virtualized infrastructures according to a green paradigm that pursues energy-efficiency. This optimization problem can be modelled as an articulated 0-1 Linear Program based on a flow model. Since the problem can prove hard to be solved by a state-of-the-art optimization software, even for instances of moderate size, we propose a new fast matheuristic for its solution. Preliminary computational tests on a set of realistic instances return encouraging results, showing that our algorithm can find better solutions in considerably less time than a state-of-the-art solver.
Keywords
This work has been partially carried out in the framework of the Labex MS2T program. Labex MS2T is supported by the French Government, through the program “Investments for the future”, managed by the French National Agency for Research (Reference ANR-11-IDEX-0004-02).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Larsson, C.: 5G Networks - Planning, Design and Optimization. Academic Press, Cambridge (2018)
Abdelwahab, S., Hamdaoui, B., Guizani, M., Znati, T.: Network function virtualization in 5G. IEEE Commun. Mag. 54(4), 84–91 (2016)
Herrera, J., Botero, J.: Resource allocation in NFV: a comprehensive survey. IEEE Trans. Netw. Serv. Manage. 13(3), 518–532 (2016)
Baumgartner, A., Bauschert, T., D’Andreagiovanni, F., Reddy, V.S.: Towards robust network slice design under correlated demand uncertainties. In: IEEE International Conference on Communications (ICC), pp. 1–7 (2018)
Luizelli, M.C., Bays, L.R., Buriol, L.S., Barcellos, M.P., Gaspary, L.P.: Piecing together the NFV provisioning puzzle: efficient placement and chaining of virtual network functions. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 98–106 (2015)
Marotta, A., D’Andreagiovanni, F., Kassler, A., Zola, E.: On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures. Comput. Netw. 125, 64–75 (2017)
Mechtri, M., Ghribi, C., Zeghlache, D.: A scalable algorithm for the placement of service function chains. IEEE Trans. Netw. Serv. Manage. 13(3), 533–546 (2016)
Marotta, A., Zola, E., D’Andreagiovanni, F., Kassler, A.: A fast robust approach for green virtual network functions deployment. J. Netw. Comput. Appl. 95, 42–53 (2017)
Blum, C., Puchinger, J., Raidl, G., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft. Comput. 11, 4135–4151 (2011)
Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton (2009)
Bauschert, T., Büsing, C., D’Andreagiovanni, F., Koster, A.M.C.A., Kutschka, M., Steglich, U.: Network planning under demand uncertainty with robust optimization. IEEE Commun. Mag. 52, 178–185 (2014)
Bertsimas, D., Sim, M.: The price of robustness. Oper. Res. 52(1), 35–53 (2004)
IBM ILOG CPLEX. http://www-01.ibm.com/software
Goldberg, D.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1988)
Danna, E., Rothberg, E., Le Pape, C.: Exploring relaxation induced neighborhoods to improve MIP solutions. Math. Program. 102, 71–90 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bauschert, T., D’Andreagiovanni, F., Kassler, A., Wang, C. (2019). A Matheuristic for Green and Robust 5G Virtual Network Function Placement. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-16692-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16691-5
Online ISBN: 978-3-030-16692-2
eBook Packages: Computer ScienceComputer Science (R0)