Ongoing Thesis

Master's Theses

End-to-End Scheduling in Large-Scale Deterministic Networks

Keywords:
TSN, Scheduling, Industrial Networks
Short Description:
To evaluate APS in TSN Networks

Description

Providing Quality of Service (QoS) to emerging time-sensitive applications such as factory automation, telesurgery, and VR/AR applications is a challenging task [1]. Time Sensitive Networks (TSN) [2] and Deterministic Networks [3] were developed for such applications to guarantee ultra low latency, bounded latency and jitter, and zero congestion loss. The objective of this work is to develop a methodology to guarantee bounded End-to-End (E2E) latency and jitter in large-scale networks.

Prerequisites

C++, Expeience with OMNET++, KNowledge of TSN.

Supervisor:

Yash Deshpande, Philip Diederich - Dr Siyu Tang (Huawei Technologies)

Student Assistant Jobs

Working Student for Network Delay Measurements

Description

Communication Networks must fulfill a strict set of requirements in the Industrial Area. The Networks must fulfill strict latency and bandwidth requirements to allow trouble-free operation. Typically, the industry relies on purpose build solutions that can satisfy the requirements.

Recently, the industry is moving towards using Ethernet-based Networks for their use case. This enables us to use common of the shelf hardware to communicate within the network. However, this hardware still will execute industrial applications and therefore has the same strict requirements as the network. In this project, we consider Linux-based hosts that run the industrial applications. We consider different networking hardware and configurations of the system to see how it affects performance. The goal is to investigate the overhead of the host. 

 

Your tasks within the project are :

  • Measure the Host Latency with different NICs
  • Measure the Host Latency with different Hardware Offloads
  • Tune, configure, and measure the Linux Scheduler to improve performance

 

You will gain:

  • Experience with Networking Hardware
  • Experience with Hardware Measurements 
  • Experience with Test Automation

 

Please send a short intro of yourself with your CV and transcript of records to us. We are looking forward to meeting you.

 

Prerequisites

  • Familiarity with Linux Console
  • Python
  • C (not required, but a plus)

Contact

philip.diederich@tum.de

Supervisor: