Dynamic Priority Assignment in FreeRTOS Kernel for Improving Performance Metrics

Salamun, Karla; Pavić, Ivan; Džapo, Hrvoje

doi:10.23919/mipro52101.2021.9596948

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Although these are valuable contributions, the proposed methods resulted in a high-overhead implementation of dynamic scheduling in FreeRTOS. In contrast, recent research, such as the works by Salamun et al [6] and Oliveira et al [7], have improved previous approaches by implementing dynamic scheduling directly into the FreeRTOS kernel. However, their studies focused on randomly generated periodic tasks instead of event-driven tasks.…”

Section: B Motivational Examplementioning

confidence: 99%

“…However, their studies focused on randomly generated periodic tasks instead of event-driven tasks. Furthermore, their implementations resulted in higher overheads on the kernel, either in terms of timing by updating ready tasks in a timebased manner rather than an event-based manner [6], or in terms of memory by adding periodic-specific parameters and using multiple lists instead of one for ready tasks [7].…”

Section: B Motivational Examplementioning

confidence: 99%

“…The periodic update method involves checking, updating, and reordering the list at fixed intervals, whereas the event-driven method only updates the list when a new task is added to it. Salamun et al [6] implement periodic updating of tasks' parameters in the ready list at every RTOS tick. However, this method introduces a large overhead in scheduling timing due to redundant processing.…”

Section: A Single Sorted List Of Ready Tasksmentioning

confidence: 99%

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Dynamic Scheduling for Event-Driven Embedded Industrial Applications

Taji,

Miranda,

Peón-Quirós

et al. 2023

2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC)

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This paper addresses the optimization of embedded platforms to meet the computing and real-time requirements of cyber-physical systems and IoT applications, including embedded intelligence. In this context, schedulers are vital in enhancing processor utilization in industrial contexts. Although existing research has focused primarily on the schedulability of periodic tasks, event-driven tasks better represent these new embedded intelligence scenarios in the real world. This work explores static and dynamic scheduling policies within a general scenario and a specific case study based on an actual industrial application. The proposed dynamic scheduler has been integrated into the FreeRTOS kernel and has been employed to conduct all of our experiments on industrial products within the smart home domain. Our results show that, while we can respect real-time requirements, our proposed dynamic scheduling can improve the performance of event-driven applications by reducing missed task deadlines by up to 60 %. Moreover, we have also developed a lightweight version of our dynamic scheduler for industrial products that reduces average timing overhead for task selection and insertion by up to 34.7 % and memory overhead for task creation and list scheduling by up to 74.7 % compared to stateof-the-art static alternatives.

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Section: B Motivational Examplementioning

confidence: 99%

Section: B Motivational Examplementioning

confidence: 99%

Section: A Single Sorted List Of Ready Tasksmentioning

confidence: 99%

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