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Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

Jaber Almutairi1, Mohammad Aldossary2,*,*

1 Department of Computer Science, College of Computer Science and Engineering, Taibah University, Al-Madinah, Saudi Arabia
2 Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

* Corresponding Author: Mohammad Aldossary. Email:

Computers, Materials & Continua 2021, 68(3), 4143-4160. https://doi.org/10.32604/cmc.2021.018145

Abstract

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network. Also, it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one. A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types. Finally, this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment.

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Cite This Article

J. Almutairi and M. Aldossary, "Investigating and modelling of task offloading latency in edge-cloud environment," Computers, Materials & Continua, vol. 68, no.3, pp. 4143–4160, 2021. https://doi.org/10.32604/cmc.2021.018145

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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