An evolutive framework for server placement optimization to digital twin networks

Author:

Xiao Lijun1,Han Dezhi1,Weng Tien‐Hsiung2,Chen Shaomiao3,Deng Han4,Souri Alireza5ORCID,Li Kuan‐Ching2ORCID

Affiliation:

1. College of Information Engineering Shanghai Maritime University Shanghai China

2. Department of Computer Science and Information Engineering Providence University Taichung Taiwan

3. School of Computer Science and Engineering Hunan University of Science and Technology Hunan China

4. Guangzhou Institute of Technology and Industry Guangzhou China

5. Department of Software Engineering Haliç University Istanbul Turkey

Abstract

SummaryDigital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real‐time data and requires interaction with the physical network in real‐time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP‐hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline‐learning‐based approach is used to reduce the search space, and a self‐examining guided local search method is proposed to improve the search efficiency. Additionally, a look‐up table‐based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3