Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata

TL;DR

This paper introduces a granularity-based framework to improve the efficiency of analyzing real-time components modeled by timed automata, balancing precision and computational complexity.

Contribution

It proposes a novel abstraction method that analyzes components at multiple granularities and derives bounds on output streams, addressing state space explosion issues.

Findings

Framework reduces analysis time significantly.

Balances analysis precision with computational efficiency.

Provides bounds on output streams using RTC theory.

Abstract

To analyze complex and heterogeneous real-time embedded systems, recent works have proposed interface techniques between real-time calculus (RTC) and timed automata (TA), in order to take advantage of the strengths of each technique for analyzing various components. But the time to analyze a state-based component modeled by TA may be prohibitively high, due to the state space explosion problem. In this paper, we propose a framework of granularity-based interfacing to speed up the analysis of a TA modeled component. First, we abstract fine models to work with event streams at coarse granularity. We perform analysis of the component at multiple coarse granularities and then based on RTC theory, we derive lower and upper bounds on arrival patterns of the fine output streams using the causality closure algorithm. Our framework can help to achieve tradeoffs between precision and analysis time.