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DOI: 10.14569/IJACSA.2023.01404102
PDF

A Particle Swarm Optimization with Imbalance Initialization and Task Rescheduling for Task Offloading in Device-Edge-Cloud Computing

Author 1: Hui Fu
Author 2: Guangyuan Li
Author 3: Fang Han
Author 4: Bo Wang

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

  • Abstract and Keywords
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Abstract: Smart devices, e.g., smart-phones, internet-of-thing device, has been prevalent in our life. How to take full advantage of the limited resources to satisfy as many as requirements of users is still a challenge. Thus, in this paper, we focus on the task offloading problem to address the challenge by device-edge-cloud computing, by PSO improved with the imbalance initialization and the task scheduling. The imbalance initialization is to increase the probability that a task is assigned to a computing node such that the node provides a longer slack time. The task scheduling is to reassign tasks with deadline violations into other nodes, to improve the number of accepted tasks for each offloading solution. Extensive experiment results show that our proposed algorithm has better performance than other ten classical and up-to-data algorithms in both the maximization of the accepted task number, the resource utilization, as well as the processing rate.

Keywords: Cloud computing; edge computing; task offloading; task scheduling; particle swarm optimization

Hui Fu, Guangyuan Li, Fang Han and Bo Wang, “A Particle Swarm Optimization with Imbalance Initialization and Task Rescheduling for Task Offloading in Device-Edge-Cloud Computing” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01404102

@article{Fu2023,
title = {A Particle Swarm Optimization with Imbalance Initialization and Task Rescheduling for Task Offloading in Device-Edge-Cloud Computing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01404102},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01404102},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Hui Fu and Guangyuan Li and Fang Han and Bo Wang}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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