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Priority Refinement for Dependent Tasks in Large Embedded Real-Time Software

Jeffrey R. Merrick, Shige Wang, Kang G. Shin

Presented at IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2005), San Francisco, California, March 7 - 10, 2005


In a large embedded real-time system, priority assignment can greatly affect the timing behavior--which can consequently affect the overall behavior--of the system. Thus, it is crucial for model-based design of a large embedded real-time system to be able to intelligently assign priorities such that tasks can meet their deadlines. In this paper, we propose a priority refinement method for dependent tasks distributed throughout a heterogeneous multiprocessor environment. In this method, we refine an initial priority assignment iteratively using the simulated annealing technique with tasks’ latest completion times (LCT). Our evaluations, based on randomly-generated models, have shown that the refinement method outperforms other priority assignment schemes and scales well for large, complex, real-time systems. This method has been implemented in the Automatic Integration of Reusable Embedded Software (AIRES) toolkit and has been successfully applied to a real vehicle system control application.

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