@phdthesis {43290,
	title = {Control Principles for Autonomous Communication Networks},
	year = {2023},
	abstract = {The growing complexity of communication networks and the explosion of network traffic have made the task of managing these networks exceedingly hard. A potential approach for striking this increasing complexity is to build an autonomous self-driving network that can measure, analyze and control itself in real time and in an automated fashion with- out direct human intervention. In this thesis, we focus on realizing such an autonomous network leveraging state-of-the-art networking technologies along with artificial intelli- gence and machine learning techniques. Toward this goal, we exploit different learning paradigms to automate network management. First, we propose supervised machine learning methods to detect increases in delays in mobile broadband networks. Further, considering the challenges of supervised learning in networking applications, we present a novel real-time distributed architecture for detecting anomalies in mobile network data in an unsupervised fashion. It also involves a collaborative framework for knowledge sharing between the distributed probes in the network to improve the overall system accuracy. Second, we propose a novel deep reinforcement learning based control framework for op- timizing resources utilization while minimizing performance degradation in multi-slice Radio Access Network (RAN) through a set of diverse control actions. We explore both centralized and distributed control architectures. Last, we design a framework for timely collecting telemetry, detecting and attributing outages in mobile networks. We evaluate our framework on a software defined virtualised testbed that resembles a cloudified mobile network.},
	author = {Azza Hassan Moh {Ahmed}}
}
@inproceedings {42509,
	title = {Crosslayer Network Outage Classification Using Machine Learning},
	journal = {Applied Networking Research Workshop (ANRW)},
	year = {2022},
	pages = {1-7},
	publisher = {ACM},
	author = {Jan Marius {Evang} and Azza Hassan Moh {Ahmed} and Elmokashfi, Ahmed and Bryhni, Haakon}
}
@inproceedings {42779,
	title = {Deep reinforcement learning-based control framework for radio access networks},
	journal = {MobiCom {\textquoteright}22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking},
	year = {2022},
	pages = {897-899},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450391818},
	doi = {10.1145/349524310.1145/3495243.3558276},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3495243https://dl.acm.org/doi/10.1145/3495243.3558276https://dl.acm.org/doi/pdf/10.1145/3495243.3558276},
	author = {Azza Hassan Moh {Ahmed} and Elmokashfi, Ahmed}
}
@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}}
}
@article {42563,
	title = {ICRAN: Intelligent Control for Self-driving RAN based on Deep Reinforcement Learning},
	journal = {IEEE Transactions on Network and Service Management},
	volume = {19},
	year = {2022},
	month = {Jan-01-2022},
	pages = {2751 - 2766},
	publisher = { IEEE},
	doi = {10.1109/TNSM.2022.3191746},
	url = {https://ieeexplore.ieee.org/document/9831432/http://xplorestaging.ieee.org/ielx7/4275028/5699970/09831432.pdf?arnumber=9831432},
	author = {Azza Hassan Moh {Ahmed} and Elmokashfi, Ahmed}
}
@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}}
}
@inproceedings {42480,
	title = {RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks},
	journal = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
	year = {2022},
	pages = {2682{\textendash}2691},
	publisher = {ACM},
	abstract = {The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed.\&nbsp; In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies.\&nbsp; It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system.\&nbsp; We implement and evaluate RCAD on real world measurements from a commercial mobile network.\&nbsp; RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods.\&nbsp;},
	author = {Azza Hassan Moh {Ahmed} and Riegler, Michael and Hicks, Steven and Elmokashfi, Ahmed}
}
@article {28345,
	title = {Predicting High Delays in Mobile Broadband Networks},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	month = {DEC-24-2021},
	pages = {168999 - 169013},
	publisher = { IEEE},
	doi = {10.1109/ACCESS.2021.3138695},
	url = {https://ieeexplore.ieee.org/document/9663160/http://xplorestaging.ieee.org/ielx7/6287639/6514899/09663160.pdf?arnumber=9663160},
	author = {Azza Hassan Moh {Ahmed} and Hicks, Steven and Riegler, Michael and Elmokashfi, Ahmed}
}
@inproceedings {28333,
	title = {Convolutional Neural Network for a Self-Driving Car in a Virtual Environment},
	journal = {2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)},
	year = {2019},
	author = {Mohamed A. A. {Babiker}},
	editor = {Mohamed A. O. {Elawad}}
}
@inproceedings {28346,
	title = {Convolutional Neural Network for a Self-Driving Car in a Virtual Environment},
	journal = {2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)},
	year = {2019},
	publisher = {IEEE},
	address = {Khartoum, Sudan},
	doi = {10.1109/ICCCEEE46830.201910.1109/ICCCEEE46830.2019.9070826},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9052497https://ieeexplore.ieee.org/document/9070826/http://xplorestaging.ieee.org/ielx7/9052497/9070322/09070826.pdf?arnumber=9070826},
	author = {Mohamed A. A. {Babiker} and Mohamed A. O. {Elawad}},
	editor = {Azza Hassan Moh {Ahmed}}
}
