Development and Evaluation of a Big Data Framework for Performance Management in Mobile Networks

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Title: Development and Evaluation of a Big Data Framework for Performance Management in Mobile Networks
Authors: Martinez-Mosquera, Diana | Navarrete, Rosa | Luján-Mora, Sergio
Research Group/s: Advanced deveLopment and empIrical research on Software (ALISoft)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Big data | Framework | Mobile networks | Network management system | Performance management
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 31-Dec-2020
Publisher: IEEE
Citation: IEEE Access. 2020, 8: 226380-226396.
Abstract: In telecommunications, Performance Management (PM) data are collected from network elements to a centralized system, the Network Management System (NMS), which acts as a business intelligence tool specialized in monitoring and reporting network performance. Performance Management files contain the metrics and named counters used to quantify the performance of the network. Current NMS implementations have limitations in scalability and support for volume, variety, and velocity of the collected PM data, especially for 5G and 6G mobile network technologies. To overcome these limitations, we proposed a Big Data framework based on an analysis of the following components: software architecture, ingestion, data lake, processing, reporting, and deployment. Our work analyzed the PM files’ format on a real data set from four different vendors and 2G, 3G, 4G, and 5G technologies. Then, we experimentally assessed our proposed framework’s feasibility through a case study involving 5G PM files. Test results of the ingestion and reporting components are presented, identifying the hardware and software required to support up to one billion counters per hour. This proposal can help telecommunications operators to have a reference Big Data framework to face the current and future challenges in the NMS, for instance, the support of data analytics in addition to the well-known services.
Sponsor: This work was supported by the Unidad de Gestión de Investigación y Proyección Social from the Escuela Politécnica Nacional.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3045175
Language: eng
Type: info:eu-repo/semantics/article
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Peer Review: si
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