Probabilistic Safe WCET Estimation for Weakly Hard Real-Time Systems at Design Stages

TL;DR

This paper presents a method for estimating safe, probabilistic WCET ranges in weakly hard real-time systems during early design stages, combining multi-objective search and logistic regression to improve accuracy and confidence.

Contribution

It introduces a novel approach that integrates multi-objective search with probabilistic regression to find safe WCET ranges, enhancing early system design analysis.

Findings

Outperforms baseline random search in estimating safe WCET ranges.

Achieves high-confidence WCET estimates within 23 hours.

Successfully applied to satellite and synthetic systems.

Abstract

Weakly hard real-time systems can, to some degree, tolerate deadline misses, but their schedulability still needs to be analyzed to ensure their quality of service. Such analysis usually occurs at early design stages to provide implementation guidelines to engineers so that they can make better design decisions. Estimating worst-case execution times (WCET) is a key input to schedulability analysis. However, early on during system design, estimating WCET values is challenging and engineers usually determine them as plausible ranges based on their domain knowledge. Our approach aims at finding restricted, safe WCET sub-ranges given a set of ranges initially estimated by experts in the context of weakly hard real-time systems. To this end, we leverage (1) multi-objective search aiming at maximizing the violation of weakly hard constraints in order to find worst-case scheduling scenarios and (2) polynomial logistic regression to infer safe WCET ranges with a probabilistic interpretation. We evaluated our approach by applying it to an industrial system in the satellite domain and several realistic synthetic systems. The results indicate that our approach significantly outperforms a baseline relying on random search without learning, and estimates safe WCET ranges with a high degree of confidence in practical time (< 23h).