Efficient mapping of runnables to tasks for embedded AUTOSAR applications

Khenfri, Fouad; Chaaban, Khaled; Chetto, Maryline

doi:10.1016/j.sysarc.2020.101800

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…This access is facilitated through the complex driver layer, allowing the implementation of intricate sensor and controller operations, such as fuel injection, ignition control, and other specific and complex functions. The complex driver layer is particularly valuable for implementing hardware resources not supported by AUTOSAR or not standardized, while ensuring compliance with real-time requirements for specific operations (15).…”

Section: 3 Base Software Layermentioning

confidence: 99%

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Yang

2024

IJE

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With the escalating integration of Electronic Control Units (ECUs) in contemporary vehicles, the intricacy of vehicle networks is incessantly advancing. Diagnostic communication, as a pivotal facet within these networks, grapples with protracted development cycles and heightened intricacies. In a bid to augment software reusability and portability, this study meticulously scrutinized pertinent research and proffered an electric vehicle fault diagnosis system predicated on the Controller Area Network (CAN) bus, leveraging the diagnostic communication architecture advocated by the AUTOSAR standard. The integration of AUTOSAR seeks to pioneer an innovative software development paradigm for automotive fault diagnosis systems, thereby remedying extant limitations. The communication and diagnostic module of this study were instantiated using AUTOSAR, thereby obviating the necessity for developers to immerse themselves in hardware intricacies and communication implementations. This allows developers to focalize their efforts on crafting software features for fault diagnosis. Empirical results illustrate that the single-core CPU utilization rate of the proposed method in this article stands at 40.68%, with a fault detection time of 0.0217. The success rate of fault detection is 98.70%, indicating an increase of 12.97% and 8.98% when compared to the CAN bus and structural analysis methods, respectively. Testing indicators are significantly mitigated, yielding more precise fault detection outcomes. The exploration of this avant-garde software development methodology in automotive electronic products markedly amplifies the efficiency of automotive troubleshooting system software, underscoring its potential for academic contribution and application in real-world scenarios.

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Section: 3 Base Software Layermentioning

confidence: 99%

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Yang

2024

IJE

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“…Other works take advantage of optimization methods to address the NP-hard problem of software resource allocation and configuration. These works adopted another perspective using optimization methods [11][12][13]19]. In [11], Thomas Wilhelm et al propose methods to optimize the mapping of runnables to tasks in multicore automotive control units.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [12] address the issues of AUTOSAR model design analysis to minimize memory stack utilization for mixed-criticality systems with preemptive threshold setting that addresses the design synthesis problem. In a prior work [19], the authors developed a heuristic algorithm to configure task mapping for AUTOSAR-based embedded systems. The main goal is to optimize the operating system and task mapping to improve system performance.…”

Section: Related Workmentioning

confidence: 99%

“…Common optimization goals are stack memory usage [11,12] using genetic algorithms and scheduling algorithms, core load balancing [11], inter-communication overhead [13,14] using integer linear programming (ILP), data consistency using ILP and heuristic methods [15], and deadline violation [16,17] using genetic algorithms and the greedy heuristic approach for system resource clustering. Other work proposes regrouping runnables with arbitrary periods on the same task using a greedy heuristic approach combined with linear cost function and schedulability test [18,19]. This approach may lead to a minimum number of executors needed to schedule overloaded systems while minimizing the stack memory usage and context switch overhead.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

Chaaban

2023

Applied Sciences

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A new version of a robot operating system (ROS-2) has been developed to address the real-time and fault constraints of distributed robotics applications. However, current implementations lack strong real-time scheduling and the optimization of response time for various tasks and applications. This may lead to inconsistent system behavior and may affect system performance. This article presents a new and efficient heuristic scheduling algorithm for the ROS-2 framework to improve resource mapping and system scheduling for robotics applications. The proposed scheduling design includes grouping callbacks into executors, assigning executor priority, and sequencing callbacks inside executors. The timing constraints and functional properties are expressed in the formal design model, and real-time scheduling is performed with respect to the timing constraints. The benefits of the proposed solution are demonstrated by a set of experimental results. Using this algorithm, it is shown that the number of executors needed to schedule a set of callbacks is minimized and the system schedulability is maximized for loaded and overloaded cases.

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Influence of Various DLT Architectures on the CPU Resources

Pankiewicz

2021

Theory and Engineering of Dependable Computer Systems and Networks

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Efficient mapping of runnables to tasks for embedded AUTOSAR applications

Cited by 7 publications

References 14 publications

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

Influence of Various DLT Architectures on the CPU Resources

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