Adaptive Task Scheduling Switcher for a Resource-Constrained IoT System

被引：0

作者：

Bin Kamilin, Mohd Hafizuddin ^{[1
]}

Bin Ahmadon, Mohd Anuaruddin ^{[1
]}

Yamaguchi, Shingo ^{[1
]}

机构：

[1] Yamaguchi Univ, Grad Sch Sci & Technol Innovat, 2-16-1 Tokiwadai, Ube, Yamaguchi 7558611, Japan

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | 2021年

关键词：

Internet of Things; Scheduling; Machine Learning; Sort and Fit;

D O I：

10.1109/ICCE50685.2021.9427674

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a novel method to use machine learning for switching the scheduling algorithm that has lower computation time and better task execution sequence optimization to meet the computation deadline. Due to the number of tasks and the number of types of resources taken will affect the computation time of the scheduler, the fixed scheduling algorithm unable to meet the computation deadline in worst-case time-complexity. Our implementation of machine learning predicts the best scheduling algorithm to counter the problem, and the result shows it can improve the accuracy of meeting the computation deadline by an average of 85.11%.

引用

页数：3

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