Orchestration of use-case driven analytics in 5G scenarios

  • Lorena Isabel Barona López
  • Jorge Maestre Vidal
  • Luis Javier García Villalba
Original Research

Abstract

The SELFNET project provides an autonomic network management framework for 5G networks with a high degree of automation, self-healing and self-optimization. These capabilities are achieved through a layered architecture and a use-case driven approach. A differentiating feature on SELFNET is its competence when creating and customizing new use cases and their related virtual functions. In this way, the use case operators are able to introduce new rules and parameters that will be taken into account in the analysis and decision-making tasks. Due these characteristics, the orchestration of its analytical functions poses an important challenge in terms of configurability, synchronization and management of resources. In order to contribute to their resolution, this paper aims to lay the groundwork for implement the design and specification of the SELFNET Analyzer orchestration. To this end, several key issues related with the internal coordination of the analytics are introduced, among them initial assumptions, design principles, limitations, partitioning of the analysis process, data persistency and optimization. The proposed orchestration strategy has been implemented with different uses cases within the SELFNET Project.

Keywords

5G Situational awareness SDN/NFV Data analysis Orchestration 

Notes

Acknowledgements

This work is supported by the European Commission Horizon 2020 Programme under grant agreement number H2020-ICT-2014-2/671672 - SELFNET (Framework for Self-Organized Network Management in Virtualized and Software Defined Networks). Lorena Isabel Barona López is supported by the Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación SENESCYT (Quito, Ecuador) under Convocatoria Abierta 2013 Scholarship Program.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431Universidad Complutense de Madrid (UCM)MadridSpain

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