Theses

Please contact our internal team members personally for additional thesis topics or send an email to dnet-teaching(at)rub.de.

Topics of ongoing and finished theses

Finding and Breaking Cyclic Dependencies between Data Flows in Random Networks
Blackbox Testing of Network Calculus Analyses
Efficient Caching of Intermediate Analyses Results
Network Analysis with the Improved TFA Algorithm
Linear Optimization in the DNC Analyses: Impact of the Solver on the Performance
A MILP Approach by Parts for a Scalable Analysis of FIFO Feedforward Networks
Improving the LUDB-FF Delay Bounds by Partial Crossflow Cutting
On the impact of the network topology on communication, exemplified by decentralized path planning for unmanned transport vehicles
Optimized Node Position Determination in null-Balanced Trees for Peer-to-Peer Networks
Improving the Min-plus-algebraic Network Calculus Analysis for Delay Bounding in FIFO Networks by Numerical Approximation
Deterministic Network Calculus for Multicasting: A Numerical Comparison Between Explicit Intermediate Bounds and Multicast Feed-Forward Analysis
Large-scale Numerical Evaluation of Network Calculus Analyses
Deterministic Performance Analysis of FIFO-multiplexing Feed-forward Networks
Modelling and Analysis of Timing Constraints of an Industrial Control System
Design, Implementation and Evaluation of the Dependency Decomposition for Nested Flows

Sample Thesis Topics (non-exhaustive, non-exclusive list)

A Heuristic for Flow Prolongation

Flow prolongation is a technique that can improve the quality of performance bounds derived with deterministic network calculus. During the analysis it is, however, not possible to know the most beneficial prolongation among all alternatives. An exhaustive enumeration was shown not scale well (see literature below).

Literature

Fabien Geyer, Alexander Scheffler and Steffen Bondorf. Network Calculus with Flow Prolongation - A Feedforward FIFO Analysis enabled by ML. In IEEE Transactions on Computers, Special Issue on Real-time Systems, 2022. [@IEEE, bib, dataset]
Fabien Geyer, Alexander Scheffler and Steffen Bondorf. Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis. In Proc. of IEEE RTAS 2021. [@IEEE, bib, dataset]
Steffen Bondorf. Better Bounds by Worse Assumptions — Improving Network Calculus Accuracy by Adding Pessimism to the Network Model. In Proc. of the IEEE International Conference on Communications (ICC), May 2017. [@IEEE]

Graph Neural Networks in Network Performance Analysis

Graph Neural Networks (GNNs) can be used to speed up the performance analysis with Deterministic Network Calculus (DNC). There are different topics available in the area of GNNs in DNC that extend the existing literature.

Literature

Fabien Geyer, Alexander Scheffler and Steffen Bondorf. Network Calculus with Flow Prolongation - A Feedforward FIFO Analysis enabled by ML. In IEEE Transactions on Computers, Special Issue on Real-time Systems, 2022. [@IEEE, bib, dataset]
Fabien Geyer and Steffen Bondorf. DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks. In Proc. of the 38th IEEE International Conference on Computer Communications (INFOCOM 2019), April 2019. [@IEEE, bib, dataset]

Do’s and Don’ts in Theses

We provide a short (not complete) list of do’s and don’ts that are relevant for writing a succesful thesis. Only accessible with your RUB account GitLab@RUB