AI Anomaly Detection for Cloudified Mobile Core Architectures

IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solut...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2023-06, Vol.20 (2), p.1976-1992
Main Authors: Michelinakis, Foivos, Pujol-Roig, Joan S., Malacarne, Sara, Xie, Min, Dreibholz, Thomas, Majumdar, Sayantini, Poe, Wint Yi, Patounas, Georgios, Guerrero, Carmen, Elmokashfi, Ahmed, Theodorou, Vasileios
Format: Article
Language:English
Subjects:
5G mobile communication
Anomalies
Anomaly detection
Artificial intelligence
autoencoders
Cloud computing
Deep learning
Degradation
mobile networks
Modules
Monitoring
Quality of service
smart networks
Test stands
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Summary:IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of NFV on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudified mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify the anomalies and the network services causing them. The deep learning solutions are tested using various artificially induced bottlenecks. The AI solutions are shown to correctly detect anomalies and identify the network components involved in the bottlenecks, with certain limitations in a particular type of bottlenecks. A discussion of the right monitoring tools to identify concrete bottlenecks is provided.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2022.3203246