SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Authors

Liangjun Song, The Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 6111731, China
Gang Sun, The Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 6111731, China & Intelligent Computing Key Laboratory of Sichuan Province, Chengdu, 6111731, Chinac The Pengcheng Labora- tory, Shenzhen, 518000, China
Hongfang Yu, The Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 6111731, China & The Pengcheng Labora- tory, Shenzhen, 518000, China
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

arXiv

Abstract

In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network. Copyright © 2022, The Authors. All rights reserved.

DOI

10.48550/arXiv.2205.03081

Publication Date

5-6-2022

Keywords

5G mobile communication systems, Energy utilization, Quality of service, Computing resource, Edge computing, Energy utilization ratio, Future networks, Key parts, Priority, Service deployment, Task offloading, Task priorities, Mobile edge computing, Networking and Internet Architecture (cs.NI)

Comments

IR Deposit conditions: non-described

Preprint available on arXiv

Recommended Citation

L. Song, G. Sun, H. Yu and M. Guizani, "SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC", arXiv, May 2022, doi: 10.48550/arXiv.2205.03081