Advertisement

Mobile Networks and Applications

, Volume 21, Issue 4, pp 564–574 | Cite as

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

  • Jose Oscar FajardoEmail author
  • Fidel Liberal
  • Ioannis Giannoulakis
  • Emmanouil Kafetzakis
  • Vincenzo Pii
  • Irena Trajkovska
  • Thomas Michael Bohnert
  • Leonardo Goratti
  • Roberto Riggio
  • Javier Garcia Lloreda
  • Pouria Sayyad Khodashenas
  • Michele Paolino
  • Pavel Bliznakov
  • Jordi Perez-Romero
  • Claudio Meani
  • Ioannis Chochliouros
  • Maria Belesioti
Article

Abstract

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

Keywords

Centralised mobile networks Light data Centre Small cells Mobile edge computing 5G 

Notes

Acknowledgments

The research leading to these results has been performed in the scope of the H2020 5G-PPP project SESAME. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671596.

References

  1. 1.
    Soldani D, Manzalini A (2015) Horizon 2020 and beyond: on the 5G operating system for a true digital society. IEEE Veh Technol Mag 10(1):32–42CrossRefGoogle Scholar
  2. 2.
    Chih-Lin I, Huang J, Duan R, Cui C, Jiang J, Li L (2014) Recent progress on C-RAN centralisation and cloudification. IEEE Access 2:1030–1039CrossRefGoogle Scholar
  3. 3.
    Wu J, Zhang Z, Hong Y, Wen Y (2015) Cloud radio access network (C-RAN): a primer. IEEE Netw 29(1):35–41CrossRefGoogle Scholar
  4. 4.
    Rost P, Bernardos CJ, Domenico AD, Girolamo MD, 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
  5. 5.
    Next Generation Mobile Networks (NGMN) Alliance (2015) Project RAN Evolution: Further Study on Critical C-RAN Technologies https://www.ngmn.org/publications/all-downloads/article/project-ran-evolution-further-study-on-critical-c-ran-technologies.html
  6. 6.
    Small Cell Forum (2015) Document 106.05.1.01: Virtualization for Small Cells: Overview. http://scf.io/doc/106
  7. 7.
    Small Cell Forum (2015) Document 159.05.1.01: Small Cell Virtualization Functional Splits and Use Cases. http://scf.io/doc/159
  8. 8.
    ETSI Industry Specification Group Mobile-edge Computing, http://www.etsi.org/technologies-clusters/technologies/mobile-edge-computing
  9. 9.
    Faizul Bari Md, Boutaba R, Esteves R, Zambenedetti Granville L, Podlesny M, Golam Rabbani Md, Zhang Q, Faten Zhani M (2013) Data center network virtualization: a survey. IEEE Communications Surveys & Tutorials 15(2):909–928Google Scholar
  10. 10.
    Hammadi A, Mhamdi L (2014) A survey on architectures and energy efficiency in data center networks. Comput Commun 40:1–21CrossRefGoogle Scholar
  11. 11.
    Jennings B, Stadler R (2015) Resource Management in Clouds: survey and research challenges. J Netw Syst Manag 23:567–619CrossRefGoogle Scholar
  12. 12.
    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13:1587–1611CrossRefGoogle Scholar
  13. 13.
    Oueis J, Calvanese Strinati E, Sardellitti E, Barbarossa S (2015) Small Cell Clustering for Efficient Distributed Fog Computing: A Multi-User Case. In 2015 I.E. 82nd Vehicular Technology Conference (VTC Fall), pp- 1-5.Google Scholar
  14. 14.
    ETSI Industry Specification Group for Network Function Virtualisation, http://www.etsi.org/technologies-clusters/technologies/nfv
  15. 15.
    3GPP TR 23.829: Local IP Access and Selected IP Traffic Offload (LIPA-SIPTO)Google Scholar
  16. 16.
    3GPP TR 23.859: LIPA Mobility and SIPTO at the Local NetworkGoogle Scholar
  17. 17.
    3GPP TR 22.828 Study on co-ordinated Packet data network GateWay (PGW) Change for Selected IP Traffic Offload (CSIPTO)Google Scholar
  18. 18.
    Taleb T, Corici M, Parada C, Jamakovic A, Ruffino S, Karagiannis G, Magedanz T (2015) EASE: EPC as a service to ease mobile core network deployment over cloud. IEEE Netw 29(2):78–88CrossRefGoogle Scholar
  19. 19.
    Service Function Chaining in Open Daylight using NSH protocol: https://wiki.opendaylight.org/view/Service_Function_Chaining:Main
  20. 20.
  21. 21.
    Anwer B, Benson T, Feamster N, Levin D (2015) Programming slick network functions. In Proc. the 1st ACM SIGCOMM Symposium on Software Defined Networking Research (SOSR '15), ACM, New York, NY, USA, article 14, 13 pagesGoogle Scholar
  22. 22.
    Riggio R. et al (2016) Programming Wireless Network Functions. In Proc. IEEE NOMS 2016, Istabul, TurkeyGoogle Scholar
  23. 23.
    Riggio R, Bradai A, Rasheed T, Ahmed T, Slawomir K, Schulz-Zander J (2015) Virtual Network Functions Orchestration in Wireless Networks. In Proc.11th International Conference on Network and Service Management (CNSM), Barcelona, IFIP/IEEEGoogle Scholar
  24. 24.
    Akyildiz IF, Wang P, Lina S-C (2015) SoftAir: a software defined networking architecture for 5G wireless systems. Comput Netw 85:1–18CrossRefGoogle Scholar
  25. 25.
    Basta A, Kellerer W, Hoffmann M, Hoffmann, K; Schmidt E-D (2013) A Virtual SDN-Enabled LTE EPC Architecture: A Case Study for S−/P-Gateways Functions. In Proc. 2013 I.E. SDN for Future Networks and Services (SDN4FNS), pp. 1–7Google Scholar
  26. 26.
    Malik A, Qadir J, Ahmad B, Alvin Yau KL, Ullah U (2015) QoS in IEEE 802.11-based wireless networks: a contemporary review. J Netw Comput Appl 55:24–46CrossRefGoogle Scholar
  27. 27.
    Chourasia S; Sivalingam KM (2015) SDN based Evolved Packet Core architecture for efficient user mobility support. In Proc. 2015 1st IEEE Conference on Network Softwarization (NetSoft) pp. 1–5Google Scholar
  28. 28.
    He J, Song W (2015) Smart routing: fine-grained stall management of video streams in mobile core networks. Comput Netw 85:51–62CrossRefGoogle Scholar
  29. 29.
    Fajardo JO, Taboada I, Liberal F (2015) Radio-aware service-level scheduling to minimize downlink traffic delay through mobile edge computing. In Proc. MONAMI 2015. LNICST 158:1–14Google Scholar
  30. 30.
    Fajardo JO, Taboada I, Liberal, F (2015) improving content delivery efficiency through multi-layer mobile edge adaptation. IEEE Network November/December 2015Google Scholar
  31. 31.
    ETSI GS NFV-MAN 001 V1.1.1 (2014) Network functions virtualisation (NFV); Management and OrchestrationGoogle Scholar
  32. 32.
    3GPP TS 28.511: Telecommunication management; Configuration Management (CM) for mobile networks that include virtualized network functions; ProceduresGoogle Scholar
  33. 33.
    Beck MT, Maier M (2014) Mobile Edge Computing: Challenges for Future Virtual Network Embedding Algorithms. In Proc. The Eighth International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2014)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jose Oscar Fajardo
    • 1
    Email author
  • Fidel Liberal
    • 1
  • Ioannis Giannoulakis
    • 2
  • Emmanouil Kafetzakis
    • 3
  • Vincenzo Pii
    • 4
  • Irena Trajkovska
    • 4
  • Thomas Michael Bohnert
    • 4
  • Leonardo Goratti
    • 5
  • Roberto Riggio
    • 5
  • Javier Garcia Lloreda
    • 6
  • Pouria Sayyad Khodashenas
    • 7
  • Michele Paolino
    • 8
  • Pavel Bliznakov
    • 8
  • Jordi Perez-Romero
    • 9
  • Claudio Meani
    • 10
  • Ioannis Chochliouros
    • 11
  • Maria Belesioti
    • 11
  1. 1.University of the Basque Country (UPV/EHU)BilbaoSpain
  2. 2.N. C.S.R. “Demokritos”Agia ParaskeviGreece
  3. 3.ORION INNOVATIONSAthensGreece
  4. 4.Zurich University of Applied Sciences (ZHAW)WinterthurSwitzerland
  5. 5.CREATE-NETTrentoItaly
  6. 6.ATOSMadridSpain
  7. 7.i2catBarcelonaSpain
  8. 8.Virtual Open SystemsGrenobleFrance
  9. 9.Universitat Politecnica de Catalunya (UPC)BarcelonaSpain
  10. 10.ITALTELSettimo MilaneseItaly
  11. 11.Hellenic Telecommunications Organization S.A. (OTE)MaroussiGreece

Personalised recommendations