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

📝 Original Info

• Title: From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis
• ArXiv ID: 2507.22645
• Date: 2025-07-30
• Authors: ** 제공되지 않음 (논문에 저자 정보가 포함되지 않았습니다.) **

📝 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.

💡 Deep Analysis

📄 Full Content

Reference