From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis

2025-07-30

AI Summary

Detecting timing constraint violations in real-time systems faces challenges of high runtime overhead and unreliable prediction under short observation windows. This paper proposes a lightweight hybrid approach that integrates low-overhead, runtime event tracing with semi-Markov chain (SMC) probabilistic modeling. System timestamped events are mapped to state transitions, and task execution time is directly characterized via the “absorption time” of the SMC—thereby reducing reliance on strong distributional assumptions. The method simultaneously captures both typical and extreme temporal behaviors within extremely short observation windows. Experimental evaluation on a real-time Linux platform demonstrates that it achieves high-accuracy worst-case execution time (WCET) estimation with less than 1% CPU overhead. The model exhibits strong interpretability and cross-layer analytical capability, significantly improving both the efficiency and practicality of timing verification.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development, but often are unable to deliver reliable predictions with limited data. This paper presents a hybrid method for worst-case execution time estimation, combining lightweight runtime tracing with probabilistic modelling. Timestamped system events are used to construct a semi-Markov chain, where transitions represent empirically observed timing between events. Execution duration is interpreted as time-to-absorption in the semi-Markov chain, enabling worst-case execution time estimation with fewer assumptions and reduced overhead. Empirical results from real-time Linux systems indicate that the method captures both regular and extreme timing behaviours accurately, even from short observation periods. The model supports holistic, low-intrusion analysis across system layers and remains interpretable and adaptable for practical use.

Problem

• Predicting worst-case execution time in real-time systems
• Reducing overhead in temporal constraint violation detection
• Combining runtime tracing with probabilistic modeling for accuracy

Innovation

• Combines runtime tracing with probabilistic modeling
• Uses semi-Markov chain for execution time estimation
• Enables accurate analysis with low overhead