Projects
Finished Research Projects
In the following, I present the projects I was either leading or worked in as a member. During my life as a PhD, I have already gained experience in BMBF, 2020 Horizon, DFG projects, Industry collaborations, etc. In particular I have gained experience in requesting funding or helping to write proposals such as BMBF, DFG, ERC 2020 Horizon, and Industry Collaboration projects.
Adversarial Design Framework for Self-Driving Networks (ADVISE)
I was the Full PI of this DFG/FWF-funded project on the German side. I was fully responsible for writing and requesting funding for this project. I finally received funding for a full PhD position for 3 years.
Summary
Inspired by self-driving cars, the networking community is currently engaged in designing more automated and ‘‘self-driving’’ communication systems, aiming to overcome the cumbersome and error-prone manual approach to manage and operate networks. Ideally, such self-driving networks also allow to exploit the increasing flexibilities introduced by emerging new Internet technologies, such as software-defined and virtualized communication technologies. With these technologies, the networks allow to meet the stringent performance requirements of new networks (e.g., 5G and 6G) and workloads (e.g., low-latency tele-operation or high-bandwidth machine-to-machine type communication), by adapting to the context and demand.
The Internet, one of the largest and most complex artefacts built by mankind, has evolved organically over the last decades, and many design choices were taken based on experience and best practices. This project proposes a novel network framework to design and operate such networks, relying on the vision of such self-driving networks, and studying how to integrate Machine Learning and Artificial Intelligence concepts into existing networks. In order to overcome the potential concerns regarding the dependability of such Artificial Intelligence and Machine Learning approaches, we envision a hybrid solution which keeps the human in the loop. Hence, we first ask three fundamental questions in this project: how predictable are today’s networks, i.e., user demands, workload traffic, and behavior of network functions? Can we make network design and algorithms data-driven and human interpretable? How to design a network framework that combines both, generative workload models and data-driven algorithms with guarantees?
The novelty of this project lies in the integration and application of Artificial Intelligence and Machine Learning on designing network algorithms. For the first time, Artificial Intelligence and Machine Learning should be integrated also in the testing and the developing phase of new networking solutions, and not only solely applied to solving problems. In terms of methodologies, we consider adversarial and game-theoretic approaches to test and optimize networks, to leverage the performance benefits from Machine Learning approaches while at the same time provide rigorous worst-case guarantees. Finally, a proof-of-concept implementation should demonstrate the new framework.
Duration
01/01/2021 - 12/31/2023
Team
- Andreas Blenk, Full PI DFG
- Stefan Schmid, Faculty of Computer Science, University of Vienna
Funding Agency
- FWF - Der Wissenschaftsfonds
- DFG - Deutsche Forschungsgemeinschaft
AI-NET PROTECT
I was the project leader of this research project. I was fully responsible for the grant writing of the Chair of Communication Networks.
Links:
Duration
01/02/2021 - 31/01/2024
Team
- Maximilian Stephan
- Patrick Kraemer
- Andreas Blenk
Funding Agency
BMBF - Bundesministerium für Bildung und Forschung
Design and Evaluation of Flexible Programmable Hybrid Real-time Networks with Hard and Soft Real-time Guarantees (SDN-APP Phase2)
I was the Full PI of this DFG-funded project. I was fully responsible for writing and requesting funding for this project. I finally received funding for a 50% PhD position for 3 years.
Summary
Software Defined Networking (SDN) marks a fundamental paradigm shift that allows integrating new concepts such as application-aware resource management in todays communication networks. Such concepts, in particular, improve the Quality-of-Experience (QoE) of user-oriented multimedia applications. The feasibility of such concept was demonstrated in Phase 1 of DFG SDN-App. The Phase 1 architecture investigated the realization of application-aware management and control on two fronts: taking network information on the application control plane into account improves the use of network resources; considering application demands on the network control plane rigorously improves application performance and QoE.
Phase 2 of DFG SDN-App consequently extends the considered scenario and architecture: besides taking care of end user-oriented multimedia applications and QoE (Phase 1), it newly integrates time-critical (industrial) services and their changing requirements cases where end users do not play a dominant role. In particular, Phase 2 focuses on hard as well as soft real-time requirements of time-critical services with dynamically changing demands like smart manufacturing to control devices. Hard real-time constraints require, e.g., a maximum end-to-delay and maximum delay jitter; soft real-time constraints manifest in stochastic guarantees, e.g., on packet loss and delay. Existing approaches suffer from unused, hence wasted network resources or the complexity to address dynamically changing demands and network reconfigurations. Real-time constraints are in trade-off to flexibility.
Applying the methodologies from analysis, simulation, to measurement and testbed implementations, the goal of Phase 2 is to realize a hybrid real-time network that provides guarantees to hard and soft real-time applications, while at the same time hosting multimedia applications. In particular, concepts such as Time Sensitive Networking (TSN) and network programmability (P4) are investigated and analyzed jointly. To evaluate the overall architecture, not only traditional measures are applied, but also new measures such as network flexibility are investigated to quantify the benefits of hybrid real-time networks under changing demands with time and cost constraints. The overall target are guidelines for selected use cases (industrial, data center, wide area network), that combine the results of Phase 1 and 2.
An extension of Phase 1 towards industrial and real-time networks is important to sustain the dynamically and faster growing demands of todays applications. A flexible architecture that provides guarantees in face of reconfigurations will allow realizing hybrid networks that can host all application types simultaneously. With the ability to program and use standard hardware for all kind of network scenarios, network operators will save operation and capital expenditures in the future.
Team
- Wolfgang Kellerer, TUM, PI
- Tobias Hossfeld, Juilius Maximilians Universität Würzburg
- Andreas Blenk, TUM, PI
Duration
11/2021 - 10/2024
Funding Agency
- DFG - Deutsche Forschungsgemeinschaft
BMBF Research Hub 6G-life
More details:
Duration
08/2021 - 08/2025
Funding Agency
BMBF
CELTIC/BMBF SENDATE Planets Pluto
I was an active team member contributing research papers. Furthermore, I was responsible for reporting and partially managing financials.
Link: LKN Webpage
Funding Agency
BMBF - Bundesministerium für Bildung und Forschung
ERC Consolidator Grant: “Quantifying Flexibility in Communication Networks (FlexNets)”
I am an active team member and helped in writing the proposal.
Link: LKN Webpage
Funding Agency
European Research Council (ERC)
Anwendungsorientierte, SDN-basierte Steuerungsarchitekturen für Kommunikationsnetze und ihre Leistungsbewertung
I was an active team member and was mainly responsible in writing the research proposal for the TUM part.
Link: LKN Webpage
Funding Agency
DFG - Deutsche Forschungsgemeinschaft
ZIM Projekt: Configuration Management in SDN
This was a joint research project together with Infosim I was an active team member in this project.
Funding Agency
BMWi - Bundesministerium für Wirtschaft und Energie