You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

TaskMUSTER - A Comprehensive Analysis of Task Parameters for Mixed Criticality Automotive Systems

Authors:

Arun Sukumaran Nair , Louella Colaco , Biju Raveendran , Sasikumar Punnekkat

Research group:

Dependable Software Engineering

Publication Type:

Journal article

Venue:

Sādhanā - Elsevier

Publisher:

Elsevier & Indian Academy of Sciences

DOI:

https://doi.org/10.1007/s12046-021-01778-y

Abstract

Automotive computing platforms are becoming complex and steadily being transformed into mixed criticality systems (MCS) with connectivity to the user, infrastructure and other vehicles. Due to the safety critical and real time nature of such systems, this transition emphasizes careful selection of task models, task scheduling mechanisms and software development practices to ensure predictability. Though there exists a wealth of research results on MCS, often the non-uniform descriptions and non-comprehensive nature of task parameters become hindrances to the designers and researchers, thus limiting their wider applicability. In this context, we carryout a comprehensive analysis of task parameters for MCS and propose a task parameter aggregator called TaskMUSTER with focus on automotive domain, specifically on the controlling domain software applications like power steering, brake systems and power-train. TaskMSUTER aims to provide a unified view of various task model parameters in terms of important attributes related to resource & communication, energy, fault-tolerance, mode change, OS overheads and parallel processing. This work also provides Backus-Naur form grammar and railroad diagram of TaskMUSTER. The usability analysis of TaskMUSTER and comparisons with well-known task model propositions are carried out using an automotive wake-up controller task set. The results justify the suitability of TaskMUSTER for designing safety certifiable automotive MCS. Overall, TaskMUSTER acts as a comprehensive and design friendly handbook for researchers and designers in the mixed criticality automotive controlling domain.

Bibtex

@article{Sukumaran Nair 6329,
author = {Arun Sukumaran Nair and Louella Colaco and Biju Raveendran and Sasikumar Punnekkat},
title = {TaskMUSTER - A Comprehensive Analysis of Task Parameters for Mixed Criticality Automotive Systems},
volume = {47},
number = {13},
pages = {1--23},
month = {January},
year = {2022},
journal = {Sādhanā - Elsevier },
publisher = {Elsevier {\&} Indian Academy of Sciences},
url = {http://www.es.mdu.se/publications/6329-}
}