Redirigiendo al acceso original de articulo en 18 segundos...
Inicio  /  Information  /  Vol: 12 Par: 4 (2021)  /  Artículo
ARTÍCULO
TITULO

Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System

Mohd Hafizuddin Bin Kamilin    
Mohd Anuaruddin Bin Ahmadon and Shingo Yamaguchi    

Resumen

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.

 Artículos similares

       
 
Caterina Feletti, Carlo Mereghetti and Beatrice Palano    
In the field of robotics, a lot of theoretical models have been settled to formalize multi-agent systems and design distributed algorithms for autonomous robots. Among the most investigated problems for such systems, the study of the Uniform Circle Forma... ver más
Revista: Applied Sciences

 
Danial Rooyani and Fantahun Defersha    
The work in this paper is motivated by a recently published article in which the authors developed an efficient two-stage genetic algorithm for a comprehensive model of a flexible job-shop scheduling problem (FJSP). In this paper, we extend the applicati... ver más
Revista: Algorithms

 
Lei Wang, Chenguang Wang and Huabing Wang    
In order to accelerate the execution of streaming applications on multi-core systems, this article studies the scheduling problem of synchronous data flow graphs (SDFG) on homogeneous multi-core systems. To describe the data flow computation process, we ... ver más
Revista: Algorithms

 
Bo Xu, Yi Hu, Menglan Hu, Feng Liu, Kai Peng and Lan Liu    
Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to near... ver más
Revista: Applied Sciences

 
June-sup Yi, Tuan Anh Luong, Hosik Chae, Min Sung Ahn, Donghun Noh, Huy Nguyen Tran, Myeongyun Doh, Eugene Auh, Nabih Pico, Francisco Yumbla, Dennis Hong and Hyungpil Moon    
This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task?s processing time, their location in the working space, depend... ver más
Revista: Applied Sciences