Efficient Deep Learning Approach for Computational Offloading in Mobile Edge Computing Networks

Xiaoliang Cheng,Zhigang Jin,Jingchun Liu,Hasan Ali Khattak

doi:10.1155/2022/2976141

Abstract

The fifth-generation mobile communication technology is broadly characterised by extremely high data rate, low latency, massive network capacity, and ultrahigh reliability. However, owing to the explosive increase in mobile devices and data, it faces challenges, such as data traffic, high energy consumption, and communication delays. In this study, multiaccess edge computing (previously known as mobile edge computing) is investigated to reduce energy consumption and delay. The mathematical model of multidimensional variable programming is established by combining the offloading scheme and bandwidth allocation to ensure that the computing task of wireless devices (WDs) can be reasonably offloaded to an edge server. However, traditional analysis tools are limited by computational dimensions, which make it difficult to solve the problem efficiently, especially for large-scale WDs. In this study, a novel offloading algorithm known as energy-efficient deep learning-based offloading is proposed. The proposed algorithm uses a new type of deep learning model: multiple-parallel deep neural network. The generated offloading schemes are stored in shared memory, and the optimal scheme is generated by continuous training. Experiments show that the proposed algorithm can generate near-optimal offloading schemes efficiently and accurately.

Highlights

Academic Editor: Hasan Ali Khattak e fifth-generation mobile communication technology is broadly characterised by extremely high data rate, low latency, massive network capacity, and ultrahigh reliability
A novel offloading algorithm known as energy-efficient deep learning-based offloading is proposed. e proposed algorithm uses a new type of deep learning model: multiple-parallel deep neural network. e generated offloading schemes are stored in shared memory, and the optimal scheme is generated by continuous training
Introduction e rapid development of fifth-generation (5G) mobile communication technology services in recent times has prompted the emergence of compute-intensive applications, such as intelligent driving, ultra-high-definition video, and mobile crowdsensing [1]. e 5G technology is largely characterised by extremely high data rate, low latency, massive network capacity, and ultrahigh reliability; it requires the appropriate architecture to function efficiently

Summary

System Model and Problem Formulation

Assuming that there is a subtask t in WD, to solve the cost function of its local execution, the computational delay and energy consumption must be first determined. Computational delay (Tr(c)) and energy consumption (Ernt) are crucial operators that affect edge offloading. E cost function of remote offloading is obtained by computational delay Tr(c) and energy consumption Ernt of edge offloading, which is denoted as Cr(s): Cr(s) c3Tr(c) + c4Ernt. E model contains four key parameters that affect the utility of the system: the number of bytes D of the subtask, the allocated communication resources, K subcarriers, and the allocated computing resources-m CPUs. e energy consumption Ent is employed to execute the task. Based on the established system utility model and the cost analysis of local offloading and edge offloading, the cost function of performing computing tasks is obtained as. We use the DNN to obtain the most approximate value Po, making Po infinitely close to P

EDLO Algorithm Design

Experiment and Comparative Analysis

Findings

Conclusion