@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}
}
@book {27852,
	title = {AI and ML {\textendash} Enablers for Beyond 5G Networks},
	year = {2021},
	month = {05/2021},
	publisher = {5G PPP Technology Board},
	organization = {5G PPP Technology Board},
	address = {Online},
	abstract = {This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from almost 20 5G PPP projects, coordinated through the 5G PPP Technology Board, that research, implement and validate 5G and B5G network systems. The paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for enhancing 5G and B5G networks.},
	doi = {10.5281/zenodo.429989},
	url = {https://5g-ppp.eu/wp-content/uploads/2021/05/AI-MLforNetworks-v1-0.pdf},
	author = {Mohammed A. {Abdulkadir} and Alay, {\"O}zg{\"u} and Alexiou, Angeliki and Anastasopoulos, Markos and Antevski, Kiril and Aumayr, Erik and Ayed, Dhouha and Baranda, Jordi and Behravesh, Rasoul and Benzaid, Chafika and Bisson, Pascal and Anne-Marie Cristina {Bosneag} and Alexandros-Apostolos A. {Boulogeorgos} and Jos{\'e} Alcaraz {Calero} and Carrozzo, Gino and Claudio Ettore {Casetti} and Caso, Giuseppe and Alberto Huertas {Celdr{\'a}n} and Chergui, Hatim and Carla Fabiana {Chiasserini} and Chinchilla-Romero, Lorena and Chollon, Geoffroy and Cogalan, Tezcan and Colman-Meixner, Carlos and Conti, Andrea and Cosmas, John and Costa-Requena, Jos{\'e} and Desai, Rachana and Devoti, Francesco and Diamanti, Maria and Dreibholz, Thomas and Elmokashfi, Ahmed and Feghhi, Saman and Garcia-Saavedra, Andres and Gavras, Anastasius and Ghoraishi, Mir and Giannopoulos, Anastasios and Goodarzi, Meysam and Gramaglia, Marco and Guimar{\~a}es, Carlos and G{\"u}rkan, G{\"u}r and He, Jiguang and Hrasnica, Halid and Jimeno, Elisa and Kakkavas, Grigorios and Kaloxylos, Alexandros and Karyotis, Vasileios and Katta, Saimanoj and Katzouris, Nikos and Takai Eddine {Kennouche} and Klinkenberg, Ralf and Kokkoniemi, Joonas and Kondepu, Koteswararao and Lazarakis, Fotis and Lekidis, Alexios and Li, Xi and Lopez, Diego and Magoula, Lina and Mahmood, Kashif and Majumdar, Sayantini and Malinverno, Marco and Mangues-Bafalluy, Josep and Mart{\'\i}nez, Ricardo and Mart{\'\i}n-P{\'e}rez, Jorge and Martrat, Josep and Meunier, Ben and Mohamed, Ramy and Morselli, Flavio and ‪Juan Jos{\'e} Ramo {Mu{\~n}oz‬} and Mu{\~n}oz-Luengo, Pablo and Daniel Camps {Muri} and Murphy, Chris and Edgardo Montes {de Oca} and Papagianni, Chrysa and Papavassiliou, Symeon and Antonio Agust{\'\i}n P {Perales} and Gregorio Mart{\'\i}nez {P{\'e}rez} and Manuel Gil {P{\'e}rez} and Wint Yi {Poe} and Prados-Garzon, Jonathan and Di Renzo, Marco and Joan Pujol {Roig} and Pedro Miguel {S{\'a}nchez} and De Schepper, Koen and Shi, Lina and Skarmeta, Antonio and Spantideas, Sotirios and Gokul Krishna {Srinivasan} and Stamou, Adamantia and Subramanya, Tejas and Trakadas, Panagiotis and Tzanakaki, Anna and Valcarenghi, Luca and Jos{\'e} Mar{\'\i}a Jor {Valero} and Videv, Stefan and Wang, Qi and Wang, Yue and Xie, Min and Xu, Zhao and Yan, Shuangyi and Yaqub, Edwin and Yesilkaya, Anil and Zanzi, Lanfranco and Zemouri, Sofiane and Zeydan, Engin and Zhang, Xun and Zhou, Xueqing}
}
@inproceedings { EuCNC2021,
	title = {An Exposed Closed-Loop Model for Customer-Driven Service Assurance Automation},
	journal = {2021 Joint European Conference on Networks and Communications \& 6G Summit (EuCNC/6G Summit)},
	year = {2021},
	month = {06/2021},
	pages = {419{\textendash}424},
	publisher = {IEEE Computer Society},
	address = {Porto, Portugal},
	abstract = {Artificial Intelligence (AI) is widely applied in telecommunications to enable zero-touch automation in network operation and service management. Due to the high complexity, deploying advanced AI mechanisms is not always feasible inside the operator{\textquoteright}s network domains. Instead, via service exposures, it becomes possible for vertical customers to integrate their external AI solutions with the network and service management system to form a closed loop (CL) and contribute to the automation process. In this paper, we propose an exposed CL model based on service exposure and apply it to automate service assurance tasks like autoscaling in a network function virtualization (NFV) system orchestrated by ETSI Open Source MANO (OSM). A testbed is built to validate the model. It collects monitoring data from the OSM monitoring module and external monitoring tools. Vertical customers drive and customize their AI solutions to aggregate these data sets and run analytics to detect and predict anomalies prepared for scaling. Preliminary analysis demonstrates the added values of customer-driven monitoring and analysis via the exposed CL.},
	keywords = {Autonomous Management, Closed Loop, Machine learning, Monitoring, Service Exposure},
	isbn = {978-1-6654-1526-2},
	doi = {10.1109/EuCNC/6GSummit51104.2021.9482533},
	author = {Xie, Min and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas and Pujol-Roig, Joan and Malacarne, Sara and Majumdar, Sayantini and Wint Yi {Poe} and Elmokashfi, Ahmed}
}
