Measuring the Throughput of quantized neural networks on P4 devices
Implement a quantized neural network in P4 and evaluate the throughput of feed-forward networks and networks with attention mechanisms on P4 hardware.
Deliberate Load-Imbalancing in Data Center Networks
Traffic Engineering, Scheduling, Data Center Networks
Goal of this thesis is the implementation and evaluation of an in-dataplane flow scheduling algorithm based on the online scheduling algorithm IMBAL in NS3.
Recently, a scalable load balancing algorithm in the dataplane has been proposed that leverages P4 to estimate the utilization in the network and assign flows to the least utilized path. This approach can be interpreted as a form of Graham's List algorithm.
In this thesis, the student is tasked to investigate how a different online scheduling algorithm called IMBAL performs compared to HULA. A prototype of IMBAL should be implemented in NS3. The tasks of this thesis are:
- Literature research and overview to online scheduling and traffic engineering in data center networks.
- Design how IMBAL can be implemented in NS3.
- Implementation of IMBAL in NS3.
- Evaluation of the implementation in NS3 with production traffic traces and comparison to HULA (a HULA implementation is provided from the chair and its implementation not part of this thesis).
Research Internships (Forschungspraxis)
Probabilistic Traffic Classification
Probabilistic Graphical Models, Markov Model, Hidden Markov Model, Machine Learning, Traffic Classification
Classification of packet level traces using Markov and Hidden Markov Models.
The goal of this thesis is the classification of packet-level traces using Markov- and Hidden Markov Model. The scenario is open-world: Traffic of specific web applications should be distinguished from all possible web-pages (background traffic). In addition, several pages should be differentiated. Examples include: Google Maps, Youtube, Google Search, Facebook, Google Drive, Instagram, Amazon Store, Amazon Prime Video, etc.