Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing

Yue Tang; Nan Guan; Xu Jiang; Zheng Dong; Wang Yi

doi:10.1109/DAC56929.2023.10248012

Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing

Yue Tang
, Nan Guan^*
, Xu Jiang
, Zheng Dong
, Wang Yi

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

8 Citations (Scopus)

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. © 2023 IEEE.

Original language	English
Title of host publication	2023 60th ACM/IEEE Design Automation Conference (DAC)
Publisher	IEEE
ISBN (Electronic)	979-8-3503-2348-1
ISBN (Print)	979-8-3503-2349-8
DOIs	https://doi.org/10.1109/DAC56929.2023.10248012
Publication status	Published - 2023
Event	60th ACM/IEEE Design Automation Conference (DAC 2023) - San Francisco, United States Duration: 9 Jul 2023 → 13 Jul 2023 https://www.dac.com/About/Conference-Archive/60th-DAC-2023

Publication series

Name	Proceedings - Design Automation Conference
ISSN (Print)	0738-100X

Conference

Conference	60th ACM/IEEE Design Automation Conference (DAC 2023)
Abbreviated title	60th DAC
Place	United States
City	San Francisco
Period	9/07/23 → 13/07/23
Internet address	https://www.dac.com/About/Conference-Archive/60th-DAC-2023

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

RGC Funding Information

RGC-funded

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10.1109/DAC56929.2023.10248012

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@inproceedings{6f65192143084ed38164f2cd8ad26266,

title = "Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing",

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. {\textcopyright} 2023 IEEE.",

author = "Yue Tang and Nan Guan and Xu Jiang and Zheng Dong and Wang Yi",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 60th ACM/IEEE Design Automation Conference (DAC 2023), 60th DAC ; Conference date: 09-07-2023 Through 13-07-2023",

year = "2023",

doi = "10.1109/DAC56929.2023.10248012",

language = "English",

isbn = "979-8-3503-2349-8",

series = "Proceedings - Design Automation Conference",

publisher = "IEEE",

booktitle = "2023 60th ACM/IEEE Design Automation Conference (DAC)",

address = "United States",

url = "https://www.dac.com/About/Conference-Archive/60th-DAC-2023",

}

Tang, Y, Guan, N, Jiang, X, Dong, Z & Yi, W 2023, Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing. in 2023 60th ACM/IEEE Design Automation Conference (DAC). Proceedings - Design Automation Conference, IEEE, 60th ACM/IEEE Design Automation Conference (DAC 2023), San Francisco, California, United States, 9/07/23. https://doi.org/10.1109/DAC56929.2023.10248012

Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing. / Tang, Yue; Guan, Nan; Jiang, Xu et al.
2023 60th ACM/IEEE Design Automation Conference (DAC). IEEE, 2023. (Proceedings - Design Automation Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing

AU - Tang, Yue

AU - Guan, Nan

AU - Jiang, Xu

AU - Dong, Zheng

AU - Yi, Wang

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2023

Y1 - 2023

N2 - 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. © 2023 IEEE.

AB - 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. © 2023 IEEE.

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U2 - 10.1109/DAC56929.2023.10248012

DO - 10.1109/DAC56929.2023.10248012

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3503-2349-8

T3 - Proceedings - Design Automation Conference

BT - 2023 60th ACM/IEEE Design Automation Conference (DAC)

PB - IEEE

T2 - 60th ACM/IEEE Design Automation Conference (DAC 2023)

Y2 - 9 July 2023 through 13 July 2023

ER -

Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing

Abstract

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