2000 character limit reached
Real-Time Systems Optimization with Black-box Constraints and Hybrid Variables
Published 21 Jan 2024 in eess.SY and cs.SY | (2401.11620v1)
Abstract: When optimizing real-time systems, designers often face a challenging problem where the schedulability constraints are non-convex, non-continuous, or lack an analytical form to understand their properties. Although the optimization framework NORTH proposed in previous work is general (it works with arbitrary schedulability analysis) and scalable, it can only handle problems with continuous variables, which limits its application. In this paper, we extend the applications of the framework NORTH to problems with a hybrid of continuous and discrete variables. This is achieved in a coordinate-descent method, where the continuous and discrete variables are optimized separately during iterations. The new framework, NORTH+, improves around 20% solution quality than NORTH in experiments.
- S. Wang, R. K. Williams, and H. Zeng, “A general and scalable method for optimizing real-time systems with continuous variables,” IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 119–132, 2023.
- S. Baruah, A. K. Mok, and L. E. Rosier, “Preemptively scheduling hard-real-time sporadic tasks on one processor,” IEEE Real-Time Systems Symposium, pp. 182–190, 1990.
- L. Thiele, S. Chakraborty, and M. Naedele, “Real-time calculus for scheduling hard real-time systems,” in IEEE International Symposium on Circuits and Systems, pp. 101–104, 2000.
- R. Henia, A. Hamann, M. Jersak, R. Racu, K. Richter, and R. Ernst, “System level performance analysis–the symta/s approach,” IEE Proceedings-Computers and Digital Techniques, vol. 152, no. 2, pp. 148–166, 2005.
- K. G. Larsen, P. Pettersson, and W. Yi, “Uppaal in a nutshell,” International Journal on Software Tools for Technology Transfer, vol. 1, pp. 134–152, 1997.
- M. Nasri, G. Nelissen, and B. B. Brandenburg, “Response-time analysis of limited-preemptive parallel dag tasks under global scheduling,” in ECRTS, 2019.
- L. Heintzman, A. Hashimoto, N. Abaid, and R. K. Williams, “Anticipatory planning and dynamic lost person models for Human-Robot search and rescue,” in IEEE International Conference on Robotics and Automation, pp. 8252–8258, May 2021.
- PerceptIn, “2021 rtss industry challenge.” http://2021.rtss.org/industry-session/, 2021.
- A. H. Simon Kramer, Dirk Ziegenbein, “Real world automotive benchmarks for free,” 2015.
- Springer New York, NY, 2006.
- P. Pillai and K. Shin, “Real-time dynamic voltage scaling for low-power embedded operating systems,” Proceedings of the eighteenth ACM symposium on Operating systems principles, 2001.
- E. Bini, G. Buttazzo, and G. Lipari, “Minimizing cpu energy in real-time systems with discrete speed management,” ACM Trans. Embed. Comput. Syst., vol. 8, pp. 31:1–31:23, 2009.
- R. I. Davis and A. Burns, “Priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems,” 2009 30th IEEE Real-Time Systems Symposium, pp. 398–409, 2009.
- Y. Zhao, R. Zhou, and H. Zeng, “An optimization framework for real-time systems with sustainable schedulability analysis,” 2020 IEEE Real-Time Systems Symposium (RTSS), pp. 333–344, 2020.
- M. Joseph and P. K. Pandya, “Finding response times in a real-time system,” Comput. J., vol. 29, pp. 390–395, 1986.
- H. Aydin, V. Devadas, and D. Zhu, “System-level energy management for periodic real-time tasks,” 2006 27th IEEE International Real-Time Systems Symposium (RTSS’06), pp. 313–322, 2006.
- S.-Y. Huang, J. Zeng, X. Deng, S. Wang, A. Sifat, B. Bharmal, J.-B. Huang, R. Williams, H. Zeng, and C. Jung, “Rtailor: Parameterizing soft error resilience for mixed-criticality real-time systems,” in 2023 IEEE Real-Time Systems Symposium (RTSS), IEEE, 2023.
- S. Lee, H. Baek, H. Woo, K. G. Shin, and J. Lee, “Ml for rt: Priority assignment using machine learning,” 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 118–130, 2021.
- Z. Bo, Y. Qiao, C. Leng, H. Wang, C. Guo, and S. Zhang, “Developing real-time scheduling policy by deep reinforcement learning,” 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 131–142, 2021.
- S. Wang, J. Chen, X. Deng, S. A. Hutchinson, and F. Dellaert, “Robot calligraphy using pseudospectral optimal control in conjunction with a novel dynamic brush model,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6696–6703, 2020.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
