A Unified Framework for 5G Network Management Tools

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Nokia Siemens Networks
  • VTT Technical Research Centre of Finland
  • Helsinki School of Economics

Abstract

Limitations in the software architecture of current network management tools such as lack of support for combined batch and real time data processing, adaptive machine learning, support for heterogeneous data sources and the fragmentation of emerging solutions needs to be addressed in order to create a solid and forward leaning foundation for implementing 5G solutions. To address these limitations, this paper introduces the extended lambda architecture (ELA). It focuses on bringing agility and continuous learning based decision making support into the design of a unified architectural framework for new network management tools by combining batch and real time data processing with adaptive machine learning in a simple Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) loop. The benefits of using this architecture are evaluated using a proof of concept (PoC) implementation of a reliable and proactive tool for detection and compensation of cell outages in a simulated 5G network.

Details

Original languageEnglish
Title of host publicationProceedings - IEEE 11th International Conference on Service-Oriented Computing and Applications, SOCA 2018
Publication statusPublished - 2 Jan 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Service-Oriented Computing and Applications - Paris, France
Duration: 20 Nov 201822 Nov 2018
Conference number: 11

Publication series

NameIEEE International Conference on Service-Oriented Computing and Applications
PublisherIEEE
ISSN (Print)2163-2871

Conference

ConferenceIEEE International Conference on Service-Oriented Computing and Applications
Abbreviated titleSOCA
CountryFrance
CityParis
Period20/11/201822/11/2018

    Research areas

  • 5G, Big data, Lambda architecture, Machine learning, Self-organizing networks

ID: 32267728