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Service-based Analytics for 5G open experimentation platforms
Ericsson Research Lab, IRL .
Simula Metropolitan Center for Digital Engineering, NOR.
Ericsson Research Lab, IRL.
University of Malaga, ESP.
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2022 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 205, article id 108740Article in journal (Refereed) Published
Abstract [en]

A scalable, flexible and reliable Analytics service has become a requirement toward building efficient Fifth Generation (5G) experimental platforms that can support a suite of end-user experiments and verticals. Our paper presents the challenges that come with designing such a service-based Analytics component, and shows how we have used it in the context of open experimental platforms in the 5GENESIS project. Our Analytics service was designed both for enabling the efficient setup and configuration of the underlying platform, and also for ensuring that it provides useful insights into the experimentation Key Performance Indicators (KPIs) toward the end-user. Thus, Analytics proved to be a useful tool across several stages, starting from ensuring correct operation during the initial phases of the network setup and continuing into the normal day-to-day experimentation. Our experiments show how the tool was used in our setup and provide information on how to apply it to different environments. The Analytics component, designed as a set of microservices that serve several goals in the analytics workflow, is also provided as open source, being part of the Open5Genesis suite.

Place, publisher, year, edition, pages
Elsevier B.V. , 2022. Vol. 205, article id 108740
Keywords [en]
5G experimental platforms, Analytics frameworks, Microservices architecture, Open source, Benchmarking, 5g experimental platform, Analytic framework, End-users, Experimental platform, Experimentation platforms, Microservice architecture, Open experimental platforms, Open-source, Service-based, User experiments, 5G mobile communication systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-89027DOI: 10.1016/j.comnet.2021.108740ISI: 000773716000004Scopus ID: 2-s2.0-85123043516OAI: oai:DiVA.org:kau-89027DiVA, id: diva2:1642483
Available from: 2022-03-07 Created: 2022-03-07 Last updated: 2022-04-28Bibliographically approved

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Brunström, Anna

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