Bottleneck Identification in Cloudified Mobile Networks Based on Distributed Telemetry

Mah Rukh, Fida ORCID: 0000-0001-7660-1150, H. Ahmed, Azza ORCID: 0000-0001-9605-4043, Dreibholz, Thomas ORCID: 0000-0002-8759-5603, Ocampo, Adres F. ORCID: 0000-0001-6926-0992, Elmokashfi, Ahmed ORCID: 0000-0001-9964-214X and I. Michelinakis, Foivos ORCID: 0000-0001-7794-7479 (2024) Bottleneck Identification in Cloudified Mobile Networks Based on Distributed Telemetry. IEEE Transaction on Mobile Computing, 23 (5). pp. 5660-5676. doi:10.1109/TMC.2023.3312051

13914 Mah Rukh Fida et al (2023) Bottleneck Identification in Cloudified Mobile Networks Based on Distributed Telemetry - Accepted version.pdf - Accepted Version
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Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system.

Item Type: Article
Article Type: Article
Additional Information: Full published version is available to current University of Gloucestershire students and staff via the IEEE database, see publisher link below.
Uncontrolled Keywords: Monitoring; Telemetry; Degradation; Cloud computing; Computer architecture; Servers; Mobile computing; Anomaly; Bottleneck; Classification; Congestion; Mobile cloud network; Telemetry
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Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Mah Rukh
Date Deposited: 17 Apr 2024 11:28
Last Modified: 18 Apr 2024 10:30

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