SAGkit: A Python SAG Toolkit for Response Time Analysis of Hybrid-Triggered Jobs

2025-11-21

AI Summary

To address the state-space explosion problem in response-time analysis (RTA) of mixed-trigger tasks under non-preemptive scheduling in distributed control systems—caused by release jitter and variable execution times—this paper proposes a precise RTA method based on scheduling abstraction graphs (SAGs). It innovatively introduces a “task absence” mechanism, the first to explicitly model task misses within the SAG framework, enabling exact joint analysis of non-preemption, release jitter, and execution-time variability. A lightweight Python toolkit implementing the method is developed. Experimental evaluation demonstrates significantly improved analysis precision and scalability over existing approaches, while maintaining acceptable time and memory overhead—making it suitable for real-time verification of complex distributed control systems. The implementation is open-source.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Distributed Problem Solving

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

For distributed control systems, modern latency-critical applications are increasingly demanding real-time guarantees and robustness. Response-time analysis (RTA) is useful for this purpose, as it helps analyze and guarantee timing bounds. However, conventional RTA methods struggle with the state-space explosion problem, especially in non-preemptive systems with release jitter and execution time variations. In this paper, we introduce SAGkit, a Python toolkit that implements the schedule-abstraction graph (SAG) framework. SAGkit novelly enables exact and sustainable RTA of hybrid-triggered jobs by allowing job absence on the SAG basis. Our experiments demonstrate that SAGkit achieves exactness with acceptable runtime and memory overhead. This lightweight toolkit empowers researchers to analyze complex distributed control systems and is open-access for further development.

Problem

• Addresses state-space explosion in response-time analysis
• Enables exact timing analysis for hybrid-triggered jobs
• Supports non-preemptive systems with execution variations

Innovation

• Implements schedule-abstraction graph framework
• Enables exact sustainable analysis hybrid-triggered jobs
• Allows job absence on SAG basis