Distributed Real-Time Systems
Overall Course Objectives
This course aims to introduce the fundamental concepts, methods, and challenges of engineering distributed real-time systems. Students will learn about real-time task models, schedulability analysis, and real-time networks, with a focus on Time-Sensitive Networking (TSN). Through exercises and a project, students will simulate scheduling or traffic shaping policies and optimize system architecture or design, such as task mapping to cores or routing of messages. Self-learning components include simulation and metaheuristic optimization methods for exercises and project work.
See course description in Danish
Learning Objectives
- Explain the key principles and definitions of distributed real-time systems engineering
- Understand the concepts and operations of real-time task models and schedulability analysis
- Apply techniques for real-time network analysis, focusing on Time-Sensitive Networking (TSN)
- Analyze scheduling algorithms and traffic shaping policies for real-time systems
- Evaluate different strategies for system optimization, including task mapping to cores and routing of messages
- Create simulations to test scheduling or traffic shaping policies in distributed real-time systems
- Apply metaheuristic optimization methods to solve complex system optimization problems
- Design distributed real-time system architectures that meet specified requirements
- Critique and optimize distributed real-time systems based on performance metrics
- Communicate complex system designs and optimizations effectively in both written and oral forms
Course Content
The course is structured into three main sections: (1) Introduction to real-time systems, focusing on the periodic task model and schedulability analysis. (2) Detailed exploration of real-time networks, especially Time-Sensitive Networking (TSN), and their importance in distributed systems. (3) Application of knowledge through exercises and a project on system simulation, scheduling policies, traffic shaping, and system optimization, including task mapping and message routing. Metaheuristics as an optimization method will be introduced for system optimization challenges.
Teaching Method
Lectures, exercises and project work