Driven by the variety of applications, there is a plenty of different network architectures including related network optimization and modeling approaches. One of the classic areas of research at the Chair is traffic management for centralized and decentralized networks, but also new topics such as Blockchain for Networking or Internet of Things represents a core competence of the Chair.
The subject of cloud networks has recently been studied by the Chair in the field of edge and fog computing. Here, placement algorithms were tested and designed. The basis for this is always a distributed architecture, in which micro data centers in addition to conventional data centers provide computing power to operate services close to the user. Thematic overlap with research directions such as quality of experience and service as well as cloud monitoring helps to generate new ideas in this field.
Due to the complexity and heterogeneity, management approaches to communication networks have become indispensable in order to adapt the networks to a wide range of applications. The Chair is engaged in research with autonomous, intelligent network management, which offers possibilities to cope with today's emerging situations such as encrypted data traffic and high data volume in the networks.
The research areas within Traffic Management are plentiful. The chair studies traffic management approaches and optimizations in terms of Quality of Experience, Context Factors, Predictive Models, traffic management in encrypted traffic, protocols, energy efficiency and machine learning. Furthermore, the area of Source Traffic Modeling provides a basis by which traffic characteristics can be modeled and generated in research in order to carry out efficient traffic management strategies.
QoE-Awareness is used to ensure the actual end-user satisfaction in the system through traffic management. For this purpose, QoE factors on different layers are monitored and appropriate actions are taken in the network to establish or maintain user satisfaction.
Context Factors form an additional class of influencing factors to design traffic management strategies. Here, device properties, environment variables and all kinds of context properties are integrated into optimization strategies to manage traffic flows and pursue different objectives such as resource or energy efficiency.
Prediction In order to make better use of traffic management measures, research is being conducted in the area of traffic prediction to estimate future traffic situations. Here statistical models are used as well as traffic analyzes to make predictions.
Encrypted Traffic A major problem in discovering services in networks today is the use of encryption to protect users' privacy. It is becoming increasingly difficult to tailor network management measures to the user because it is not clear what applications users are currently using. There is a trade-off between maintaining the privacy and the efficiency of management decisions. Often it is however possible, while preserving privacy, to gain information about traffic behavior in order to support network management actions.
Protocols form the backbone of traffic management. They allow the transport of information and the consistent implementation of distributed management actions. The Chair is therefore studying various protocols for making traffic management decisions.
Source Traffic Models provide the basis by which traffic characteristics can be generated and studied in research in order to carry out efficient traffic management strategies. They serve as guidelines for the generation of certain traffic patterns during the performance investigations as well as standardization.
Energy Efficiency One objective of traffic management approaches is energy efficiency. The chair conducts research on how energy efficiency in networks can be maintained through intelligent traffic management algorithms.
Machine Learning A new field of traffic management measures are automated approaches that learn with the help of machine learning, network characteristics and the effects of management measures. The lessons learned form the improvements for traffic management algorithms to respond efficiently in dynamic and heterogeneous networks.
A blockchain, originally block chain, is a growing list of records, called blocks, which are linked using cryptography. It can be used to support network management approaches, new network architectures and various communication strategies. The Chair conducts research mainly in the field of modeling for blockchain architectures, and maps common blockchain algorithms in queuing models to evalute the performance of different forms of blockchain.
(October 2019 - July 2020)
The objective of the “WebQKAI” project is to infer web QoE key performance indicators (KPIs) from data collected by network devices, which provide insights for operators with respect to network operations and maintenance.
(July 2019 - September 2020)
This project examines the usage of AI methods for the parametrization of convergent, deterministic, industrial networks.
5CALE - Massive scaling of fully virtualized 5G mobile core networks in the context of IoT
(Februray 2019 - January 2022)
Within the project, new scaling methods and resource management approaches are being developed for the next generation 5G mobile communication network with IoT traffic. It is funded within the 5G call "Digitale Offensive" of the Bavarian Ministry of Economic Affairs with a total budget of one million euros and a term of 3 years.
1.Hoßfeld, T., Wunderer, S., Beyer, A., Hall, A., Schwind, A., Gassner, C., Guillemin, F., Wamser, F., Wascinski, K., Hirth, M., Seufert, M., Casas, P., Tran-Gia, P., Robitza, W., Wascinski, W., Ben Houidi, Z.: White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges. Würzburg (2020).
2.Moldovan, C., Loh, F., Seufert, M., Hoßfeld, T.: Optimizing HAS for 360-Degree Videos. 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet). , Budapest, Hungary (2020).
3.Wassermann, S., Seufert, M., Casas, P., Li, G., Kuang, L.: Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming, (2019).
4.Wamser, F., Tran-Gia, P., Geissler, S., Hoßfeld, T.: Modeling of Traffic Flows in Internet of Things Using Renewal Approximation. International Conference on Optimization and Decision Science (ODS2019) (2019).
5.Geissler, S., Prantl, T., Lange, S., Wamser, F., Hoßfeld, T.: Discrete-Time Analysis of the Blockchain Distributed Ledger Technology. 31st International Teletraffic Congress (ITC). , Budapest, Hungary (2019).