TY - CONF
T1 - Exact Schedulability Analysis for Limited-Preemptive Parallel Applications Using Timed Automata in UPPAAL
AU - Hansen, Jonas
AU - Srinivasan, Srinidhi
AU - Nelissen, Geoffrey
AU - Larsen, Kim Guldstrand
PY - 2025/3/31
Y1 - 2025/3/31
N2 - We study the problem of verifying schedulability and ascertaining response time bounds of limited-preemptive parallel applications with uncertainty, scheduled on multi-core platforms. While sufficient techniques exist for analysing schedulability and response time of parallel applications under fixed-priority scheduling, their accuracy remains uncertain due to the lack of a scalable and exact analysis that can serve as a ground-truth to measure the pessimism of existing sufficient analyses. In this paper, we address this gap using formal methods. We use Timed Automata and the powerful UPPAAL verification engine to develop a generic approach to model parallel applications and provide a scalable and exact schedulability and response time analysis. This work establishes a benchmark for evaluating the accuracy of both existing and future sufficient analysis techniques. Furthermore, our solution is easily extendable to more complex task models thanks to its flexible model architecture.
AB - We study the problem of verifying schedulability and ascertaining response time bounds of limited-preemptive parallel applications with uncertainty, scheduled on multi-core platforms. While sufficient techniques exist for analysing schedulability and response time of parallel applications under fixed-priority scheduling, their accuracy remains uncertain due to the lack of a scalable and exact analysis that can serve as a ground-truth to measure the pessimism of existing sufficient analyses. In this paper, we address this gap using formal methods. We use Timed Automata and the powerful UPPAAL verification engine to develop a generic approach to model parallel applications and provide a scalable and exact schedulability and response time analysis. This work establishes a benchmark for evaluating the accuracy of both existing and future sufficient analysis techniques. Furthermore, our solution is easily extendable to more complex task models thanks to its flexible model architecture.
U2 - 10.23919/DATE64628.2025.10992936
DO - 10.23919/DATE64628.2025.10992936
M3 - Paper without publisher/journal
ER -