Process Network Models for Embedded System Design Based on the Real-Time BIP Execution Engine

Fotios Gioulekas
(Aristotle University of Thessaloniki)
Peter Poplavko
(Mentor. A Siemens Business)
Panagiotis Katsaros
(Aristotle University of Thessaloniki)
Pedro Palomo
(Deimos-Space S.L.U.)

Existing model-based processes for embedded real-time systems support the analysis of various non-functional properties, most notably schedulability, through model checking, simulation or other means. The analysis results are then used for modifying the system's design, so that the expected properties are satisfied. A rigorous model-based design flow differs in that it aims at a system implementation derived from high-level models by applying a sequence of semantics-preserving transformations. Properties established at any design step are preserved throughout the subsequent steps including the executable implementation. We introduce such a design flow using a process network model of computation for application design at a high level, which combines streaming and reactive control processing with task parallelism. The schedulability of the so-called FPPNs (Fixed Priority Process Networks) is well-studied and various solutions have been presented. This article focuses on the design flow's steps for deriving executable implementations on the BIP (Behavior - Interaction - Priority) runtime environment. FPPNs are designed using the TASTE toolset, a convenient architecture description interface. In this way, the developers do not program explicitly low-level real-time OS services and the schedulability properties are guaranteed throughout the design steps by construction. The approach has been validated on the design of a real spacecraft on-board application that has been scheduled for execution on an industrial multicore platform.

In Simon Bliudze and Saddek Bensalem: Proceedings of the 1st International Workshop on Methods and Tools for Rigorous System Design (MeTRiD 2018), Thessaloniki, Greece, 15th April 2018, Electronic Proceedings in Theoretical Computer Science 272, pp. 79–92.
Published: 25th June 2018.

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