Publications
Proceedings, refereed
A Live Demonstration of In-Band Telemetry in OSM-Orchestrated Core Networks
In Proceedings of the 47th IEEE Conference on Local Computer Networks (LCN). Edmonton, Alberta/Canada: IEEE, 2022.Status: Published
A Live Demonstration of In-Band Telemetry in OSM-Orchestrated Core Networks
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 – 5G – 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.
| Afilliation | Communication Systems |
| Project(s) | 5G-VINNI: 5G Verticals INNovation Infrastructure , NorNet, The Center for Resilient Networks and Applications, The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | Proceedings of the 47th IEEE Conference on Local Computer Networks (LCN) |
| Pagination | 245–247 |
| Date Published | 09/2022 |
| Publisher | IEEE |
| Place Published | Edmonton, Alberta/Canada |
| ISBN Number | 978-1-6654-8001-7 |
| Keywords | Anomaly detection, Network Function Virtualisation (NFV), Open Source MANO (OSM), P4, Telemetry |
Crosslayer Network Outage Classification Using Machine Learning
In Applied Networking Research Workshop (ANRW). ACM, 2022.Status: Published
Crosslayer Network Outage Classification Using Machine Learning
| Afilliation | Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications, GAIA |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | Applied Networking Research Workshop (ANRW) |
| Pagination | 1-7 |
| Publisher | ACM |
Deep reinforcement learning-based control framework for radio access networks
In MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. New York, NY, USA: ACM, 2022.Status: Published
Deep reinforcement learning-based control framework for radio access networks
| Afilliation | Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking |
| Pagination | 897-899 |
| Publisher | ACM |
| Place Published | New York, NY, USA |
| ISBN Number | 9781450391818 |
| URL | https://dl.acm.org/doi/proceedings/10.1145/3495243https://dl.acm.org/doi... |
| DOI | 10.1145/349524310.1145/3495243.3558276 |
RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
In ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022.Status: Published
RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
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. 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. It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system. We implement and evaluate RCAD on real world measurements from a commercial mobile network. RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | The Center for Resilient Networks and Applications, Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
| Pagination | 2682–2691 |
| Publisher | ACM |
Talks, contributed
Detecting Issues with In-Band Telemetry in OSM-Orchestrated Core Networks
In ETSI, Virtual. Virtual: ETSI, 2022.Status: Published
Detecting Issues with In-Band Telemetry in OSM-Orchestrated Core Networks
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' 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.
| Afilliation | Communication Systems |
| Project(s) | SMIL: SimulaMet Interoperability Lab, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications, NorNet, 5G-VINNI: 5G Verticals INNovation Infrastructure |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | ETSI, Virtual |
| Publisher | ETSI |
| Place Published | Virtual |
| Type of Talk | Demo presentation |
| 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... |
Journal Article
ICRAN: Intelligent Control for Self-driving RAN based on Deep Reinforcement Learning
IEEE Transactions on Network and Service Management 19, no. 3 (2022): 2751-2766.Status: Published
ICRAN: Intelligent Control for Self-driving RAN based on Deep Reinforcement Learning
| Afilliation | Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Network and Service Management |
| Volume | 19 |
| Issue | 3 |
| Pagination | 2751 - 2766 |
| Date Published | Jan-01-2022 |
| Publisher | IEEE |
| URL | https://ieeexplore.ieee.org/document/9831432/http://xplorestaging.ieee.o... |
| DOI | 10.1109/TNSM.2022.3191746 |
Journal Article
Predicting High Delays in Mobile Broadband Networks
IEEE Access 9 (2021): 168999-169013.Status: Published
Predicting High Delays in Mobile Broadband Networks
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Access |
| Volume | 9 |
| Pagination | 168999 - 169013 |
| Date Published | DEC-24-2021 |
| Publisher | IEEE |
| URL | https://ieeexplore.ieee.org/document/9663160/http://xplorestaging.ieee.o... |
| DOI | 10.1109/ACCESS.2021.3138695 |
Proceedings, refereed
Convolutional Neural Network for a Self-Driving Car in a Virtual Environment
In 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 2019.Status: Published
Convolutional Neural Network for a Self-Driving Car in a Virtual Environment
| Afilliation | Machine Learning |
| Project(s) | No Simula project |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) |
Convolutional Neural Network for a Self-Driving Car in a Virtual Environment
In 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). Khartoum, Sudan: IEEE, 2019.Status: Published
Convolutional Neural Network for a Self-Driving Car in a Virtual Environment
| Afilliation | Machine Learning |
| Project(s) | No Simula project |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) |
| Publisher | IEEE |
| Place Published | Khartoum, Sudan |
| URL | https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9052497http... |
| DOI | 10.1109/ICCCEEE46830.201910.1109/ICCCEEE46830.2019.9070826 |