Annals of Telecommunications

, Volume 74, Issue 9–10, pp 559–574 | Cite as

Algorithms for the design of 5G networks with VNF-based Reusable Functional Blocks

  • Luca ChiaraviglioEmail author
  • Fabio D’Andreagiovanni
  • Simone Rossetti
  • Giulio Sidoretti
  • Nicola Blefari-Melazzi
  • Stefano Salsano
  • Carla-Fabiana Chiasserini
  • Francesco Malandrino
``CfP: Techniques for Smart and Secure 5G Softwarized Networks''


We face the problem of designing a 5G network composed of Virtual Network Function (VNF)-based entities, called Reusable Functional Blocks (RFBs). RFBs provide a high level of flexibility and scalability, which are recognized as core functions for the deployment of the forthcoming 5G technology. Moreover, the RFBs can be run on different HardWare (HW) and SoftWare (SW) execution environments located in 5G nodes, in line with the current trend of network softwarization. After overviewing the considered RFB-based 5G network architecture, we formulate the problem of minimizing the total costs of a 5G network composed of RFBs and physical 5G nodes. Since the presented problem is NP-Hard, we derive two algorithms, called SFDA and 5G-PCDA, to tackle it. We then consider a set of scenarios located in the city of San Francisco, where the positions of the users and the set of candidate sites to host 5G nodes have been derived from the WeFi app. Our results clearly show the trade-offs that emerge between (i) the total costs incurred by the installation of the 5G equipment, (ii) the percentage of users that are served, and (iii) the minimum downlink traffic provided to the users.


5G networks 5G Design CAPEX reduction 5G performance evaluation Network softwarization 


Funding information

This work has received funding from the Horizon 2020 EU project SUPERFLUIDITY (grant agreement no. 671566).


  1. 1.
    View on 5g architecture (version 2.0). (Date last accessed Sep 2017)Google Scholar
  2. 2.
    Galis A, Clayman S, Mamatas L, Loyola JR, Manzalini A, Kuklinski S, Serrat J, Zahariadis T (2013) Softwarization of future networks and services-programmable enabled networks as next generation software defined networks. In: Future networks and services (SDN4FNS), 2013 IEEE SDN for. IEEE, pp 1–7Google Scholar
  3. 3.
    Rost P, Banchs A, Berberana I, Breitbach M, Doll M, Droste H, Mannweiler C, Puente MA, Samdanis K, Sayadi B (2016) Mobile network architecture evolution toward 5g. IEEE Commun Mag 54 (5):84–91CrossRefGoogle Scholar
  4. 4.
    Rost P, Mannweiler C, Michalopoulos DS, Sartori C, Sciancalepore V, Sastry N, Holland O, Tayade S, Han B, Bega D et al (2017) Network slicing to enable scalability and flexibility in 5g mobile networks. IEEE Commun Mag 55(5):72–79CrossRefGoogle Scholar
  5. 5.
    Bianchi G, Biton E, Blefari-Melazzi N, Borges I, Chiaraviglio L, Cruz Ramos P, Eardley P, Fontes F, McGrath MJ, Natarianni L et al (2016) Superfluidity: a flexible functional architecture for 5g networks. Trans Emerg Telecommun Technol 27(9):1178–1186CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Chiaraviglio L, Amorosi L, Cartolano S, Blefari-Melazzi N, Dell’Olmo P, Shojafar M, Salsano S (2017) Optimal superfluid management of 5G networks. In: 3Rd network softwarization, IEEE conference on (IEEE netsoft).IEEE, pp 1–9Google Scholar
  8. 8.
    Chiaraviglio L, D’Andreagiovanni F, Siderotti G, Melazzi NB, Salsano S (2018) Optimal design of 5g superfluid networks: Problem formulation and solutions. In: 21St conference on innovation in clouds, internet and networks (ICIN) 2018Google Scholar
  9. 9.
    Michalopoulos DS, Doll M, Sciancalepore V, Bega D, Schneider P, Rost P (2017) Network slicing via function decomposition and flexible network design. In: Workshop on new radio technologies co-located with IEEE PIMRC. IEEE, MontrealGoogle Scholar
  10. 10.
    Basta A, Kellerer W, Hoffmann M, Morper HJ, Hoffmann K (2014) Applying nfv and sdn to lte mobile core gateways, the functions placement problem. In: Proceedings of the 4th Workshop on All Things Cellular: Operations, Applications, and Challenges, pp 33–38Google Scholar
  11. 11.
    Luizelli MC, Bays LR, Buriol LS, Barcellos MP, Gaspary LP (2015) Piecing together the NFV provisioning puzzle: Efficient placement and chaining of virtual network functions. In: 2015 IFIP/IEEE international symposium on Integrated network management (IM). IEEE, pp 98–106Google Scholar
  12. 12.
    Yousaf FZ, Loureiro P, Zdarsky F, Taleb T, Liebsch M (2015) Cost analysis of initial deployment strategies for virtualized mobile core network functions. IEEE Commun Mag 53(12):60–66CrossRefGoogle Scholar
  13. 13.
    Ananth M, Sharma R (2017) Cost and performance analysis of network function virtualization based cloud systems. In: 2017 IEEE 7th international Advance computing conference (IACC). IEEE, pp 70–74Google Scholar
  14. 14.
    Chen M, Zhang Y, Hu L, Taleb T, Sheng Z (2015) Cloud-based wireless network: virtualized, reconfigurable, smart wireless network to enable 5g technologies. Mob Netw Appl 20(6):704–712CrossRefGoogle Scholar
  15. 15.
    Sun S, Kadoch M, Gong L, Rong B (2015) Integrating network function virtualization with sdr and sdn for 4g/5g networks. IEEE Netw 29(3):54–59CrossRefGoogle Scholar
  16. 16.
    Rost P, Bernardos CJ, De Domenico A, Di Girolamo M, Lalam M, Maeder A, Sabella D, Wübben D (2014) Cloud technologies for flexible 5G radio access networks. IEEE Commun Mag 52(5):68–76CrossRefGoogle Scholar
  17. 17.
    ETSI GS NFV 002 (2014) Network functions virtualisation (NFV); architectural framework v 1.2.1. ETSIGoogle Scholar
  18. 18.
    Marzetta TL (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wirel Commun 9(11):3590–3600CrossRefGoogle Scholar
  19. 19.
    Hoydis J, Ten Brink S, Debbah M (2013) Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?. IEEE J Sel Areas Commun 31(2):160–171CrossRefGoogle Scholar
  20. 20.
    Wu J, Zhang Z, Hong Y, Wen Y (2015) Cloud radio access network (c-ran): a primer. IEEE Netw 29(1):35–41CrossRefGoogle Scholar
  21. 21.
    D’Andreagiovanni F, Caire G (2016) An unconventional clustering problem: user service profile optimization. In: 2016 IEEE international symposium on Information theory (ISIT)IEEE, pp 855–859Google Scholar
  22. 22.
    Kellerer H, Pferschy U, Pisinger D (2004) Knapsack problems. Springer, BerlinCrossRefzbMATHGoogle Scholar
  23. 23.
    Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, New YorkzbMATHGoogle Scholar
  24. 24.
    Malandrino F, Chiasserini C-F, Kirkpatrick S (2018) Cellular network traces towards 5g: usage, analysis and generation. IEEE Trans Mob Comput 17(3):529–542CrossRefGoogle Scholar
  25. 25.
    Chiaraviglio L, Blefari-Melazzi N, Chiasserini CF, Iatco B, Malandrino F, Salsano S (2017) An economic analysis of 5G Superfluid networks. In: 2017 IEEE 18th international conference on High performance switching and routing (HPSR). IEEE, pp 1–7Google Scholar
  26. 26.
    Coffman EG Jr, Csirik J, Galambos G, Martello S, Vigo D (2013) Bin packing approximation algorithms: survey and classification. Handbook of combinatorial optimization, pp 455–531Google Scholar

Copyright information

© Institut Mines-Télécom and Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luca Chiaraviglio
    • 1
    • 2
    Email author
  • Fabio D’Andreagiovanni
    • 3
    • 4
  • Simone Rossetti
    • 1
    • 2
  • Giulio Sidoretti
    • 1
    • 2
  • Nicola Blefari-Melazzi
    • 1
    • 2
  • Stefano Salsano
    • 1
    • 2
  • Carla-Fabiana Chiasserini
    • 5
    • 6
  • Francesco Malandrino
    • 6
  1. 1.Consorzio Nazionale Interunivesitario per le TelecomunicazioniRomeItaly
  2. 2.EE DepartmentUniversity of Rome Tor VergataRomeItaly
  3. 3.National Center for Scientific Research (CNRS)ParisFrance
  4. 4.CNRS, Heudiasyc UMR 7253Sorbonne Universités, Université de Technologie de CompiègneCompiègneFrance
  5. 5.DET DepartmentPolytechnic Univerisity of TurinTorinoItaly
  6. 6.IEIIT National Research Center (CNR)RomeItaly

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