TaskMUSTER: a comprehensive analysis of task parameters for mixed criticality automotive systems
ARUN SUKUMARAN NAIR LOUELLA MESQUITA COLACO BIJU RAVEENDRAN SASIKUMAR PUNNEKKAT
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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, taskscheduling 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 carry out 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.
ARUN SUKUMARAN NAIR1 LOUELLA MESQUITA COLACO1 BIJU RAVEENDRAN1 SASIKUMAR PUNNEKKAT2
Volume 48, 2023
Continuous Article Publishing mode
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