Control Performance-aware AUTOSAR Runnable Scheduling

Daeho, Choi; Lee, Gun-Min; Wootae, Jeon; Kim, Jong-Chan

doi:10.7467/ksae.2020.28.1.009

Cited by 1 publication

(4 citation statements)

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“…This section specifically evaluates our optimization method for the general DAG model. Readers interested in the evaluation results for the LPG and LMG models are referred to [27]. More specifically, we evaluate our optimization method by answering the following questions:…”

Section: Discussionmentioning

confidence: 99%

“…With this motivation, runnable periods optimization problem was first formulated in our previous research paper by Kim et al [5], which proposed a combinatorial search method that is useful only for small systems due to its high computing demands. Choi et al [27] partly solved the scalability problem using an analytical method only for limited application structures. These two papers are precursors to this paper and will be thoroughly explained even in more depth and detail in Sections 4 and 5 for the self-completeness of this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, we can find high-quality solutions close to the real optimal runnable periods. Interested readers are referred to our previous work [27].…”

Section: Finding the Optimal Runnable Periodsmentioning

confidence: 99%

“…Figure 6. Linear multipath graph (LMG) model with m independent paths identified by edges with different colors[27].…”

mentioning

confidence: 99%

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AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

Daeho¹,

Kim

2020

Applied Sciences

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When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%