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Period optimization for hard real-time distributed automotive systems
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 44th annual Design Automation Conference table of contents
San Diego, California
SESSION: Distributed computing: automotive network design and analysis table of contents
Pages: 278 - 283  
Year of Publication: 2007
ISBN ~ ISSN:0738-100X , 978-1-59593-627-1
Authors
Abhijit Davare  University of California, Berkeley
Qi Zhu  University of California, Berkeley
Marco Di Natale  General Motors Research
Claudio Pinello  Cadence Berkeley Labs
Sri Kanajan  General Motors Research
Alberto Sangiovanni-Vincentelli  University of California, Berkeley
Sponsors
: The EDA Consortium
: IEEE/CASS/CANDE/CEDA
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 37,   Citation Count: 5
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ABSTRACT

The complexity and physical distribution of modern active-safety automotive applications requires the use of distributed architectures. These architectures consist of multiple electronic control units (ECUs) connected with standardized buses. The most common configuration features periodic activation of tasks and messages coupled with run-time priority-based scheduling. The correct deployment of applications on such architectures requires end-to-end latency deadlines to be met. This is challenging since deadlines must be enforced across a set of ECUs and buses, each of which supports multiple functionality. The need for accommodating legacy tasks and messages further complicates the scenario.

In this work, we automatically assign task and message periods for distributed automotive systems. This is accomplished by leveraging schedulability analysis within a convex optimization framework to simultaneously assign periods and satisfy end-to-end latency constraints. Our approach is applied to an industrial case study as well as an example taken from the literature and is shown to be both effective and efficient.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Abhijit Davare: colleagues
Qi Zhu: colleagues
Marco Di Natale: colleagues
Claudio Pinello: colleagues
Sri Kanajan: colleagues
Alberto Sangiovanni-Vincentelli: colleagues