Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System
In this journal, we proposed a novel method of using multi-task learning to switch the scheduling algorithm. With multi-task learning to change the scheduling algorithm inside the scheduling framework, the scheduling framework can create a scheduler with the best task execution optimization under th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/4/150 |
| _version_ | 1797539203963158528 |
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| author | Mohd Hafizuddin Bin Kamilin Mohd Anuaruddin Bin Ahmadon Shingo Yamaguchi |
| author_facet | Mohd Hafizuddin Bin Kamilin Mohd Anuaruddin Bin Ahmadon Shingo Yamaguchi |
| author_sort | Mohd Hafizuddin Bin Kamilin |
| collection | DOAJ |
| description | In this journal, we proposed a novel method of using multi-task learning to switch the scheduling algorithm. With multi-task learning to change the scheduling algorithm inside the scheduling framework, the scheduling framework can create a scheduler with the best task execution optimization under the computation deadline. With the changing number of tasks, the number of types of resources taken, and computation deadline, it is hard for a single scheduling algorithm to achieve the best scheduler optimization while avoiding the worst-case time complexity in a resource-constrained Internet of Things (IoT) system due to the trade-off in computation time and optimization in each scheduling algorithm. Furthermore, different hardware specifications affect the scheduler computation time differently, making it hard to rely on Big-O complexity as a reference. With multi-task learning to profile the scheduling algorithm behavior on the hardware used to compute the scheduler, we can identify the best scheduling algorithm. Our benchmark result shows that it can achieve an average of 93.68% of accuracy in meeting the computation deadline, along with 23.41% of average optimization. Based on the results, our method can improve the scheduling of the resource-constrained IoT system. |
| first_indexed | 2024-03-10T12:41:54Z |
| format | Article |
| id | doaj.art-bfc4bc94434c4485a17a0275797be090 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T12:41:54Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-bfc4bc94434c4485a17a0275797be0902023-11-21T13:50:20ZengMDPI AGInformation2078-24892021-04-0112415010.3390/info12040150Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT SystemMohd Hafizuddin Bin Kamilin0Mohd Anuaruddin Bin Ahmadon1Shingo Yamaguchi2Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 753-8511, JapanGraduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 753-8511, JapanGraduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 753-8511, JapanIn this journal, we proposed a novel method of using multi-task learning to switch the scheduling algorithm. With multi-task learning to change the scheduling algorithm inside the scheduling framework, the scheduling framework can create a scheduler with the best task execution optimization under the computation deadline. With the changing number of tasks, the number of types of resources taken, and computation deadline, it is hard for a single scheduling algorithm to achieve the best scheduler optimization while avoiding the worst-case time complexity in a resource-constrained Internet of Things (IoT) system due to the trade-off in computation time and optimization in each scheduling algorithm. Furthermore, different hardware specifications affect the scheduler computation time differently, making it hard to rely on Big-O complexity as a reference. With multi-task learning to profile the scheduling algorithm behavior on the hardware used to compute the scheduler, we can identify the best scheduling algorithm. Our benchmark result shows that it can achieve an average of 93.68% of accuracy in meeting the computation deadline, along with 23.41% of average optimization. Based on the results, our method can improve the scheduling of the resource-constrained IoT system.https://www.mdpi.com/2078-2489/12/4/150Internet of Thingsschedulingmachine learningsort and fitoptimization |
| spellingShingle | Mohd Hafizuddin Bin Kamilin Mohd Anuaruddin Bin Ahmadon Shingo Yamaguchi Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System Information Internet of Things scheduling machine learning sort and fit optimization |
| title | Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System |
| title_full | Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System |
| title_fullStr | Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System |
| title_full_unstemmed | Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System |
| title_short | Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System |
| title_sort | multi task learning based task scheduling switcher for a resource constrained iot system |
| topic | Internet of Things scheduling machine learning sort and fit optimization |
| url | https://www.mdpi.com/2078-2489/12/4/150 |
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