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1.	Tang, Yue, et al. (author) Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing 2023 In: 2023 60th ACM/IEEE Design Automation Conference (DAC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350323481 - 9798350323498 Conference paper (peer-reviewed)abstract Many real-time systems process and react to external events by a chain of tasks, and have constraints on the maximum reaction time which describes how long it takes to respond to an external event. While a processing chain typically starts with a sampling task periodically triggered to sample the sensor data, other tasks in the chain could be triggered in two different ways: event-triggered or time-triggered, which have their own pros and cons. In this paper, we propose the third option to trigger the processing tasks in a chain, namely, the event-triggered with data refreshing approach, which combines the benefits of the event-triggered or time-triggered approaches. As the main technical contribution, we develop techniques to formally upper-bound its maximum reaction time and analytically compare it with the existing approaches. Experiments with synthetic workload are conducted to show the performance improvement by our proposed techniques.

Tang, Yue, et al. (author)
Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing
2023
In: 2023 60th ACM/IEEE Design Automation Conference (DAC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350323481 - 9798350323498
Conference paper (peer-reviewed)abstract
- Many real-time systems process and react to external events by a chain of tasks, and have constraints on the maximum reaction time which describes how long it takes to respond to an external event. While a processing chain typically starts with a sampling task periodically triggered to sample the sensor data, other tasks in the chain could be triggered in two different ways: event-triggered or time-triggered, which have their own pros and cons. In this paper, we propose the third option to trigger the processing tasks in a chain, namely, the event-triggered with data refreshing approach, which combines the benefits of the event-triggered or time-triggered approaches. As the main technical contribution, we develop techniques to formally upper-bound its maximum reaction time and analytically compare it with the existing approaches. Experiments with synthetic workload are conducted to show the performance improvement by our proposed techniques.

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