Automated Generation of Adversarial Inputs for Data Center Networks
adversarial; datacenter networks
Today's Data Center (DC) networks are facing increasing demands and a plethora of requirements. Factors for this are the rise of Cloud Computing, Virtualization and emerging high data rate applications such as distributed Machine Learning frameworks.
Many proposal for network designs and routing algorithms covering different operational goals and requirements have been proposed.
This variety makes it hard for operators to choose the ``right'' solution.
Recently, some works proposed that automatically generate adversarial input to networks or networking algorithms [1,2] to identify weak spots in order to get a better view of their performance and help operators' decision making. However, they focus on specific scenarios.
The goal of this thesis is to develop or extend such mechanisms so that they can be applied a wider range of scenarios than previously.
The thesis builds upon an existing flow-level simulator in C++ and initial algorithms that generate adversarial inputs for networking problems.
 S. Lettner and A. Blenk, “Adversarial Network Algorithm Benchmarking,” in Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies, Orlando FL USA, Dec. 2019, pp. 31–33, doi: 10.1145/3360468.3366779.
 J. Zerwas et al., “NetBOA: Self-Driving Network Benchmarking,” in Proceedings of the 2019 Workshop on Network Meets AI & ML - NetAI’19, Beijing, China, 2019, pp. 8–14, doi: 10.1145/3341216.3342207.
- Profound knowledge in C++
Research Internships (Forschungspraxis)
Towards Digital Network Twins: Can we Machine Learn Network Function Behaviors?
Digital Network Twins can help to improve future network operation and management significantly.
A Digital Twin (DT) of a network is a digital representation that is coupled to the real network.
It can be used to perform experiments e.g. to improve the operation of the real network.
Running a detailed model of a network as a DT can be quite challenging.
Either the computational effort is too high to run the model somehow in real-time or the abstraction level is too high so the DT does not represent the real network closely enough.
Here Machine Learning (ML) could be a solution. Instead of accurately modeling the behavior analytically, an ML approach observes and learns the behavior of a network and its elements and may lead to a model that is less complex to use for a DT.
Research in this direction is still at its infancy, however.
This research internship should investigate the ability of current ML approaches to learn the behavior of network functions.
For this task, Kubernetes' ingress controller, a load balancer (LB), shall be set up as an exemplary network function in a virtual testbed.
The setup should further contain traffic generators to benchmark network functions and monitoring installations to observe the be-
havior of the LB. The collected data should then be used to train an ML model.
The core question is whether how far the behavior of network functions can be learned and abstracted with ML models.
- Basic knowledge ML