TY - JOUR
T1 - Estimating Probabilistic Safe WCET Ranges of Real-Time Systems at Design Stages
AU - Lee, Jaekwon
AU - Shin, Seung Yeob
AU - Nejati, Shiva
AU - Briand, Lionel
AU - Parache, Yago Isasi
N1 - Publisher Copyright:
© 2023 Association for Computing Machinery.
PY - 2023/3/29
Y1 - 2023/3/29
N2 - Estimating worst-case execution time (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines and, hence, operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence.
AB - Estimating worst-case execution time (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines and, hence, operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence.
KW - Schedulability analysis
KW - machine learning
KW - meta-heuristic search
KW - search-based software engineering
KW - worst-case execution time
UR - http://www.scopus.com/inward/record.url?scp=85153701965&partnerID=8YFLogxK
U2 - 10.1145/3546941
DO - 10.1145/3546941
M3 - Article
AN - SCOPUS:85153701965
SN - 1049-331X
VL - 32
JO - ACM Transactions on Software Engineering and Methodology
JF - ACM Transactions on Software Engineering and Methodology
IS - 2
M1 - 37
ER -