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Towards a Cloud-Native Radio Access Network

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Advances in Mobile Cloud Computing and Big Data in the 5G Era

Part of the book series: Studies in Big Data ((SBD,volume 22))

Abstract

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers, e.g. Mobile Network Operator (MNO), Mobile Virtual Network Operator (MVNO), move from proprietary and bespoke hardware and software platforms towards an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently materialized through cloudification by leveraging the existing infrastructure. In this work, we present a Radio Access Network as a Service (RANaaS), in which a Cloudified Centralized Radio Access Network (C-RAN) is delivered as a service. RANaaS describes the service life-cycle of an on-demand, elastic, and pay as you go RAN instantiated on top of the cloud infrastructure. Due to short deadlines in many examples of RAN, the fluctuations of processing time, introduced by the virtualization framework, have a deep impact on the C-RAN performance. While in typical cloud environments, the deadlines of processing time cannot be guaranteed, the cloudification of C-RAN, in which signal processing runs on general purpose processors inside Virtual Machines (VMs), is a challenging subject. We describe an example of real-time cloudified LTE network deployment using the OpenAirInterface (OAI) LTE implementation and OpenStack running on commodity hardware. We also show the flexibility and performance of the platform developed. Finally, we draw general conclusions on the RANaaS provisioning problem in future 5G networks.

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Notes

  1. 1.

    Different protocols have been standardized for the fronthaul, namely CPRI representing 4/5 of the market, OBSAI representing 1/5 of the market, and more recently the Open Radio Interface (ORI) initiated by NGMN and now by the European Telecommunications Standards Institute (ETSI) Industry Specification Group (ISG).

  2. 2.

    http://www.kernel.org/doc/Documentation/scheduler/sched-deadline.txt.

  3. 3.

    http://www.linux-kvm.org.

  4. 4.

    http://www.xenserver.org.

  5. 5.

    http://linuxcontainers.org.

  6. 6.

    http://www.docker.com.

  7. 7.

    This rule was established empirically, because in full load conditions (i.e., all PRBs allocated in the subframe; the same MCS for all PRBs) the OAI LTE FDD TX requires 2 times less processing time than the OAI LTE FDD RX.

  8. 8.

    https://svn.eurecom.fr/openair4G/trunk/openair1/PHY/TOOLS/time_meas.h.

  9. 9.

    http://en.wikipedia.org/wiki/Haswell_(microarchitecture).

  10. 10.

    The CCDF plot for a given processing time value displays the fraction of subframes with execution times exceeding this value.

  11. 11.

    Micro-service architecture is in opposition to the so-called “monolithic” architecture where all functionality is offered by a single logical executable, see http://martinfowler.com/articles/microservices.html. It has to be noted that the micro-service architecture supports the ETSI NFV architecture [10], where each VNF can be seen as a service.

  12. 12.

    A cloud region is an organizational unit of the cloud containing a pool of cloud workers with specific properties such as the same configuration or geographical location.

  13. 13.

    http://www.ubuntu.com/cloud/tools/juju.

  14. 14.

    http://www.ubuntu.com/cloud/tools/maas.

  15. 15.

    Here, the work stack does not refer to Heat and should be understood as a protocol stack.

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Correspondence to Eryk Schiller .

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Nikaein, N., Schiller, E., Favraud, R., Knopp, R., Alyafawi, I., Braun, T. (2017). Towards a Cloud-Native Radio Access Network. In: Mavromoustakis, C., Mastorakis, G., Dobre, C. (eds) Advances in Mobile Cloud Computing and Big Data in the 5G Era. Studies in Big Data, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-45145-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-45145-9_8

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