Definition of Task Allocation and Priority Assignment in Hard Real-Time Distributed Systems. Zheng, W., Zhu, Q., Natale, M. D., & Vincentelli, A. S. In 28th IEEE International Real-Time Systems Symposium (RTSS 2007), pages 161–170, December, 2007. ISSN: 1052-8725doi abstract bibtex The complexity and physical distribution of modern active safety, chassis and powertrain automotive applications requires the use of distributed architectures. Complex functions designed as networks of function blocks exchanging signal information are deployed onto the physical HW and implemented in a SW architecture consisting of a set of tasks and messages. The typical configuration features priority-based scheduling of tasks and messages and imposes end- to-end deadlines. In this work, we optimize the task placement and the signal to message mapping and we automate the assignment of priorities to tasks and messages in order to meet end-to-end deadline constraints and minimize latencies. This is accomplished by leveraging worst case response time analysis within a mixed integer linear optimization framework. Our approach is applied to an automotive case study to prove its feasibility.
@inproceedings{zheng_definition_2007,
title = {Definition of {Task} {Allocation} and {Priority} {Assignment} in {Hard} {Real}-{Time} {Distributed} {Systems}},
doi = {10.1109/RTSS.2007.40},
abstract = {The complexity and physical distribution of modern active safety, chassis and powertrain automotive applications requires the use of distributed architectures. Complex functions designed as networks of function blocks exchanging signal information are deployed onto the physical HW and implemented in a SW architecture consisting of a set of tasks and messages. The typical configuration features priority-based scheduling of tasks and messages and imposes end- to-end deadlines. In this work, we optimize the task placement and the signal to message mapping and we automate the assignment of priorities to tasks and messages in order to meet end-to-end deadline constraints and minimize latencies. This is accomplished by leveraging worst case response time analysis within a mixed integer linear optimization framework. Our approach is applied to an automotive case study to prove its feasibility.},
booktitle = {28th {IEEE} {International} {Real}-{Time} {Systems} {Symposium} ({RTSS} 2007)},
author = {Zheng, Wei and Zhu, Qi and Natale, Marco Di and Vincentelli, Alberto Sangiovanni},
month = dec,
year = {2007},
note = {ISSN: 1052-8725},
keywords = {Automotive applications, Automotive engineering, Communication system control, Delay, Mechanical power transmission, Processor scheduling, Real time systems, Safety, Signal mapping, Timing},
pages = {161--170},
}
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