Open Access

ARTICLE

A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

Shengyao Sun^1,2, Ying Du³, Jiajun Chen⁴, Xuan Zhang⁵, Jiwei Zhang^6,*, Yiyi Xu⁷

1 School of Information Science and Technology, Zhengzhou Normal University, Zhengzhou, 450044, China
2 Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China
3 School of Geography and Tourism, Zhengzhou Normal University, Zhengzhou, 450044, China
4 Science and Engineering College, South China University of Technology, Guangzhou, 510641, China
5 Department of Electrical and Electronic Engineering, Luohe Vocational Technology College, Luohe, 462002, China
6 School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
7 Cardiff School of Engineering, Cardiff University, Cardiff, CF10 3XQ 15, UK

* Corresponding Author: Jiwei Zhang. Email:

(This article belongs to the Special Issue: Innovations in Pervasive Computing and Communication Technologies)

Computers, Materials & Continua 2023, 76(3), 2879-2900. https://doi.org/10.32604/cmc.2023.037483

Received 05 November 2022; Accepted 07 April 2023; Issue published 08 October 2023

Abstract

In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for offloading. Based on fully considering the competition between multiple UEs for communication resources and MEC service resources, CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’ resources. It achieves its goal by selecting reasonable modules to offload and using the cooperation of UE, MEC, and Cloud Center to determine the execution location of the modules. Extensive experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6% on average, task completion delay reducing around 5% on average, and better task execution success rate than other similar methods.

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Keywords

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