@article { TNSM2022,
	title = {AI Anomaly Detection for Cloudified Mobile Core Architectures},
	journal = {Transactions on Network and Service Management},
	year = {2022},
	month = {08/2022},
	publisher = { IEEE},
	address = {Los Alamitos, California/U.S.A.},
	abstract = {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.},
	keywords = {5G, AI, Anomaly detection, Autoencoders, deep learning, Mobile networks, Smart Networks},
	issn = {1932-4537},
	doi = {10.1109/TNSM.2022.3203246},
	author = {Foivos Ioannis {Michelinakis} and Joan Sebasti{\`a} {Pujol-Roig} and Malacarne, Sara and Xie, Min and Dreibholz, Thomas and Majumdar, Sayantini and Wint Yi {Poe} and Patounas, Georgios and Guerrero, Carmen and Elmokashfi, Ahmed and Theodorou, Vasileios}
}
@inproceedings { EuCNC2020,
	title = {AI-Driven Closed-Loop Service Assurance with Service Exposures},
	journal = {Proceedings of the 29th IEEE European Conference on Networks and Communications (EuCNC)},
	year = {2020},
	month = {06/2020},
	pages = {265-270},
	publisher = {IEEE Computer Society},
	address = {Dubrovnik, Dubrovnik-Neretva/Croatia},
	abstract = {Artificial Intelligence (AI) is widely applied in mobile and wireless networks to enhance network operation and service management. Advanced AI mechanisms often require high level of network service exposure in order to access data from as many network elements as possible and execute the AI recommended outcomes into the networks. However, in practice, it is not always feasible to expose the network services to 3rd parties or customers with AI ambitions. Considering that service assurance (SA) is a major area to which AI is applied, this paper describes how a closed-loop SA architecture is associated with the service exposure model in the 5G networks with network slicing. Then we investigate the impact and implication of service exposure on SA. Finally, a set of experiment results are provided to demonstrate the trade-off relationship between the AI ambition and the exposure level in SA.},
	keywords = {AI, Service Assurance, Service Exposure},
	author = {Xie, Min and Pujol-Roig, Joan and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas and Guerrero, Carmen and Sanchez, Adrian and Wint Yi {Poe} and Wang, Yue and Elmokashfi, Ahmed}
}
