The 13th International Real-Time Scheduling Open Problems Seminar (RTSOPS 2024) is a venue dedicated to exchanging ideas and fostering the discussion of open problems in real-time systems.
The seminar comprises short presentation sessions to encourage intensive discussion, new collaborations, and cooperation within the real-time systems community. RTSOPS focuses on practical aspects of real-time systems design, analysis, and open theoretical problems. We particularly encourage researchers working in industry or collaborating with industrial partners to share their challenges and open problems with us.
Abstracts may be submitted describing well-known but as yet unsolved problems. However, all abstracts should contain some element of original work that has not been published before; for example, a new problem, a new way of looking at an existing problem, new intuition or ideas on how a problem might be solved, possible frameworks for solutions, or overviews of special cases that may be useful in solving a problem.
Of course, the authors of open problem submissions from previous years’ RTSOPS are invited to submit short status reports on progress made towards a solution to their open problem. These abstracts should include a brief statement of the open problem and a description of the technical advances toward a solution.
Authors of accepted abstracts are expected to give a brief presentation/pitch of their open problem and be prepared to interact with the audience in the discussions afterward.
RTSOPS 2024 invites extended abstracts of open problems in areas such as, but not limited to:
Single-core, multi-core, and many-core scheduling and resource management
New models and analysis techniques for real-time systems
Timing analysis for heterogeneous platforms with hardware acceleration
Time-constrained cyber-physical systems
Issues in applying real-time models and analysis to industrial applications
Interactions between WCET (worst-case execution time) analysis and scheduling
Use of Machine Learning and AI in real-time systems and scheduling theory
Use of real-time scheduling theory for Machine Learning and AI problems