@article { ToMC2023,
	title = {Bottleneck Identification in Cloudified Mobile Networks based on Distributed Telemetry},
	journal = {Transactions on Mobile Computing},
	year = {2023},
	pages = {1{\textendash}18},
	publisher = {IEEE},
	abstract = {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.},
	keywords = {Anomaly, Bottleneck, classification, congestion, Mobile Cloud Network, Telemetry},
	issn = {1558-0660},
	doi = {10.1109/TMC.2023.3312051},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10239332},
	author = {Fida, Mah-rukh and Azza Hassan Moh {Ahmed} and Dreibholz, Thomas and Andr{\'e}s Felipe {Ocampo} and Ahmed Mustafa {Elmokashfi} and Foivos Ioannis {Michelinakis}}
}
@techreport {43215,
	title = {Norske mobilnett i 2022},
	journal = {Tilstandsrapport fra Simula Metropolitan Center for Digital Engineering, Center for Resilient Networks and Applications},
	year = {2023},
	note = {},
	month = {05/2023},
	publisher = {Simula},
	address = {Oslo, Norway},
	abstract = {Denne rapporten er utarbeidet av Center for Resilient Networks and Applications (CRNA), som er en del av Simula Metropolitan Center for Digital Engineering. CRNA driver grunnleggende forskning innen robusthet og sikkerhet i nettverk med mandat og finansiering fra kommunal- og moderniseringsdepartementet. Senteret produserer en {\r a}rlig rapport om tilstanden i norske mobilnett. {\r A}rets rapport er den tiende i rekken.},
	keywords = {Mobile networks, Performance, Stability},
	isbn = {978-82-92593-38-7},
	url = {https://www.simulamet.no/sites/default/files/norske_mobilnett2022.pdf},
	author = {Cicic, Tarik and Kvalbein, Amund and Anas Saeed {Al-Selwi} and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas}
}
@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}
}
@misc { OSMHackfest13,
	title = {Detecting Issues with In-Band Telemetry in OSM-Orchestrated Core Networks},
	howpublished = {ETSI, Virtual},
	year = {2022},
	publisher = {ETSI},
	type = {Demo presentation},
	address = {Virtual},
	abstract = {Open Source MANO is a helpful tool to manage and orchestrate the instantiation of core network setups, like Network Service (NS) instances of our SimulaMet OpenAirInterface Virtual Network Function (VNF) for Enhanced Packet Cores (EPC). We furthermore extended our NS with VNF instances of Programming Protocol-independent Packet Processors (P4) switches, in order to allow for in-band telemetry. With in-band telemetry, it is possible to flexibly add, process, and remove telemetry information to traffic within the packet core, in order to allow for fine-granular evaluation of the system performance and the users{\textquoteright} experienced quality of service. In our presentation and demo, we would like to provide an overview of our ongoing work on P4-based in-band telemetry in an OSM-orchestrated 4G core, which is used for detecting performance problems and anomalies in the network based on machine learning. We would furthermore like to demonstrate the details of our setup to the audience in a live demo.},
	keywords = {Anomaly detection, Network Function Virtualisation (NFV), Open Source MANO (OSM), P4, Telemetry},
	url = {http://osm-download.etsi.org/ftp/osm-11.0-eleven/OSM13_Ecosystem_Day/OSM\%2313\%20Ecosystem\%20Day\%20-\%20SIMULA.pdf},
	author = {Dreibholz, Thomas and Azza Hassan Moh {Ahmed} and Fida, Mah-rukh and Andr{\'e}s Felipe {Ocampo} and Foivos Ioannis {Michelinakis}}
}
@inproceedings { LCN2022-DemoPaper,
	title = {A Live Demonstration of In-Band Telemetry in OSM-Orchestrated Core Networks},
	journal = {Proceedings of the 47th IEEE Conference on Local Computer Networks~(LCN)},
	year = {2022},
	month = {09/2022},
	pages = {245{\textendash}247},
	publisher = { IEEE},
	address = {Edmonton, Alberta/Canada},
	abstract = {Network Function Virtualization is a key enabler to building future mobile networks in a flexible and cost-efficient way. Such a network is expected to manage and maintain itself with least human intervention. With early deployments of the fifth generation of mobile technologies {\textendash} 5G {\textendash} around the world, setting up 4G/5G experimental infrastructures is necessary to optimally design Self-Organising Networks~(SON). In this demo, we present a custom small-scale 4G/5G testbed. As a step towards self-healing, the testbed integrates four Programming Protocol-independent Packet Processors (P4) virtual switches, that are placed along interfaces between different components of transport and core network. This demo not only shows the administration and monitoring of the Evolved Packet Core (EPC) VNF components, using Open Source MANO, but also as a proof of concept for the potential of P4-based telemetry in detecting anomalous behaviour of the mobile network, such as a congestion in the transport part.},
	keywords = {Anomaly detection, Network Function Virtualisation (NFV), Open Source MANO (OSM), P4, Telemetry},
	isbn = {978-1-6654-8001-7},
	author = {Dreibholz, Thomas and Fida, Mah-rukh and Azza Hassan Moh {Ahmed} and Andr{\'e}s Felipe {Ocampo} and Foivos Ioannis {Michelinakis}}
}
@techreport { CRNA-Rapport2021,
	title = {Norske mobilnett i 2021 {\textendash} Tilstandsrapport fra Centre for Resilient Networks and Applications},
	year = {2022},
	publisher = {Simula Metropolitan Center for Digital Engineering, Centre for Resilient Networks and Applications~(CRNA)},
	address = {Oslo/Norway},
	abstract = {Denne rapporten er utarbeidet av Center for Resilient Networks and Applications (CRNA), som er en del av Simula Metropolitan Center for Digital Engineering. CRNA driver grunn- leggende forskning innen robusthet og sikker- het i nettverk med mandat og finansiering fra Kommunal- og moderniseringsdepartementet. Senteret produserer en {\r a}rlig rapport om tilstan- den i norske mobilnett. {\r A}rets rapport er den niende i rekken.},
	isbn = {82-92593-36-5},
	url = {https://www.simula.no/sites/default/files/norske_mobilnett_i_2021.pdf},
	author = {Elmokashfi, Ahmed and Kvalbein, Amund and Anas Saeed {Al-Selwi} and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas}
}
@article {43040,
	title = {Sectors, Beams and Environmental Impact on the Performance of Commercial 5G mmWave Cells: An Empirical Study},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	month = {12/2022},
	pages = {133309-133323},
	publisher = { IEEE},
	abstract = {millimeter wave (mmWave) communication is one of the cornerstones of future generations of mobile networks. While the performance of mmWave links has been thoroughly investigated by simulations and testbeds, the behavior of this technology in real-world commercial setups has not yet been thoroughly documented. In this paper, we address this gap and present the results of an empirical study to determine the actual performance of a commercial 5G mmWave cell through on-field measurements. We evaluate the signal and beam coverage map of an operational network as well as the end-to-end communication performance of a 5G mmWave connection, considering various scenarios, including human body blockage effects, foliage-caused and rain-induced attenuation, and water surface effects. To the best of our knowledge, this paper is the first to report on a commercial deployment while not treating the radio as a black box. Measurement results are compared with 3GPP{\textquoteright}s statistical channel models for mmWave to check the possible gaps between simulated and actual\&nbsp;performance. This measurement analysis provides valuable information for researchers and 5G verticals to fully understand how a 5G mmWave commercial access network operates in the real-world.},
	keywords = {5G, commercial 5G networks, coverage analysis, millimeter-wave, mmWave},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2022.3229588},
	url = {https://ieeexplore.ieee.org/document/9987496},
	author = {Moheb, Salman and Foivos Ioannis {Michelinakis} and Elmokashfi, Ahmed and Gr{\o}ndalen, Ole and Mahmood, Kashif and Zanella, Andrea}
}
@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}
}
@article {jsan10010021,
	title = {Optimising Performance for NB-IoT UE Devices through Data Driven Models},
	journal = {Journal of Sensor and Actuator Networks},
	volume = {10},
	number = {1},
	year = {2021},
	publisher = {MDPI},
	address = {Journal of Sensor and Actuator Networks},
	abstract = {This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.},
	issn = {2224-2708},
	doi = {{10.3390/jsan10010021},
	url = {https://www.mdpi.com/2224-2708/10/1/21},
	author = {Nassef, Omar and Mahmoodi, Toktam and Foivos Ioannis {Michelinakis} and Mahmood, Kashif and Elmokashfi, Ahmed}
}
@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}
}
@article {27603,
	title = {Dissecting Energy Consumption of NB-IoT Devices Empirically},
	journal = {IEEE Internet of Things Journal},
	volume = {8},
	year = {2020},
	month = {08/2020},
	pages = {1224 - 1242},
	publisher = { IEEE},
	abstract = {3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy efficient connectivity option to the rapidly expanding market of Internet of Things (IoT) devices. To unleash its full potential, end-devices are expected to work in a plug and play fashion, with zero or minimal configuration of parameters, still exhibiting excellent energy efficiency. We performed the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our findings prove that parameters{\textquoteright} settings does impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Further, we investigated which device-reported metadata metrics better reflected performance and implemented an algorithm to automatically identify device state in current time series logs. We worked with two major western European operators to provide a measurement-driven analysis of the energy consumption and network performance of two popular NB-IoT boards under different parameter configurations. We observed that energy consumption is mostly affected by the paging interval in Connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters{\textquoteright} settings can yield great energy savings.},
	keywords = {energy consumption, Internet of things, LTE, NB-IoT},
	issn = {2327-4662},
	doi = {10.1109/JIOT.2020.3013949},
	author = {Foivos Ioannis {Michelinakis} and Anas Saeed {Al-Selwi} and Capuzzo, Martina and Zanella, Andrea and Mahmood, Kashif and Elmokashfi, Ahmed}
}
@inproceedings {26612,
	title = {Towards Closed Loop 5G Service Assurance Architecture for Network Slices as a Service},
	journal = {European Conference on Networks and Communications (EuCNC)},
	year = {2019},
	pages = {139-143},
	publisher = {IEEE},
	abstract = {5G intends to use network slicing to support multiple vertical industries. The dynamic resource sharing and diverse customer requirements bring new challenges towards service assurance (SA), such as automation and customer-centric. As a response to these challenges, this paper proposes a hierarchical, modular, distributed, and scalable SA architecture. This paper highlights an important key feature\&nbsp;SA coordination, which is facilitated by three new SA functions,\&nbsp;SA interpretation,\&nbsp;SA policy management, anddata fabric. Three closed-loops are introduced to coordinate and realize automation of service management. Challenges associated with realizing SA are briefly discussed and will be addressed by leveraging the 5G infrastructure developed within the H2020-ICT-17 project 5G-VINNI.},
	author = {Xie, Min and Wint Yi {Poe} and Wang, Yue and Andr{\'e}s Javier {Gonz{\'a}lez} and Elmokashfi, Ahmed and Joao Antonio Pe {Rodrigues} and Foivos Ioannis {Michelinakis}}
}
@inproceedings {25796,
	title = {The Cloud that Runs the Mobile Internet: A Measurement Study of Mobile Cloud Services},
	journal = {IEEE INFOCOM 2018 - IEEE Conference on Computer Communications},
	year = {2018},
	month = {05/2018},
	pages = {1619-1627},
	publisher = {IEEE},
	abstract = {Mobile applications outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today{\textquoteright}s mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today{\textquoteright}s mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85\% of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver.},
	keywords = {cellular networks, cloud service providers, content distribution networks, network measurement},
	doi = {10.1109/INFOCOM.2018.8485872},
	author = {Foivos Ioannis {Michelinakis} and Doroud, Hossein and Razaghpanah, Abbas and Lutu, Andra and Vallina-Rodriguez, Narseo and Gill, Phillipa and Widmer, Joerg}
}
@article {bui2018fine,
	title = {Fine-grained LTE radio link estimation for mobile phones},
	journal = {Pervasive and Mobile Computing},
	volume = {49},
	year = {2018},
	pages = {76{\textendash}91},
	publisher = {Elsevier},
	author = {Bui, Nicola and Foivos Ioannis {Michelinakis} and Widmer, Joerg}
}
@misc {23521,
	title = {Mobile Capacity Measurements and Estimation},
	howpublished = {Fornebu, Akershus/Norway},
	year = {2014},
	type = {Proceedings of the 2nd International NorNet Users Workshop (NNUW-2)},
	author = {Foivos Ioannis {Michelinakis}}
}
