Schedulability Analysis for Automated Implementations of Real-Time Object-Oriented Models
Abstract
The increasing complexity of real-time software has led to a recent trend in the use of high-level modeling languages for development of real-time software. One representative example is the modeling language ROOM (real-time object-oriented modeling), which provides features such as object-orientation, state machine description of behaviors, formal semantics for executability of models, and possibility of automated code generation. However, these modeling languages largely ignore the timeliness aspect of real-time systems, and fail to provide any guidance for a designer to a priori predict and analyze temporal behavior. In this paper we consider schedulability analysis for automated implementations of ROOM models, based on the ObjecTime toolset. This work builds on results presented in [8], where we developed some guidelines for the design and implementation of real-time object-oriented models. Using the guidelines, we have modified the run-time system library provided by the ObjecTime toolset to make it amenable to schedulability analysis. Based on the modified toolset, we show how a ROOM model can be analyzed for schedulability, taking into account the implementation overheads and structure. The analysis is validated experimentally, first using simple periodic models, and then using a large case study of a train tilting system.