Joint optimization scheme for task offloading and resource allocation based on MO-MFEA algorithm in intelligent transportation scenarios

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications Pub Date : 2024-10-10 DOI:10.1016/j.jnca.2024.104039

Mingyang Zhao, Chengtai Liu, Sifeng Zhu

{"title":"Joint optimization scheme for task offloading and resource allocation based on MO-MFEA algorithm in intelligent transportation scenarios","authors":"Mingyang Zhao, Chengtai Liu, Sifeng Zhu","doi":"10.1016/j.jnca.2024.104039","DOIUrl":null,"url":null,"abstract":"<div><div>With the surge of transportation data and diversification of services, the resources for data processing in intelligent transportation systems become more limited. In order to solve this problem, this paper studies the problem of computation offloading and resource allocation adopting edge computing, NOMA communication technology and edge(content) caching technology in intelligent transportation systems. The goal is to minimize the time consumption and energy consumption of the system for processing structured tasks of terminal devices by jointly optimizing the offloading decisions, caching strategies, computation resource allocation and transmission power allocation. This problem is a mixed integer nonlinear programming problem that is nonconvex. In order to solve this challenging problem, proposed a multi-task multi-objective optimization algorithm (MO-MFEA-S) with adaptive knowledge migration based on MO-MFEA. The results of a large number of simulation experiments demonstrate the convergence and effectiveness of MO-MFEA-S.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104039"},"PeriodicalIF":7.7000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Network and Computer Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1084804524002169","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}

引用次数: 0

Abstract

With the surge of transportation data and diversification of services, the resources for data processing in intelligent transportation systems become more limited. In order to solve this problem, this paper studies the problem of computation offloading and resource allocation adopting edge computing, NOMA communication technology and edge(content) caching technology in intelligent transportation systems. The goal is to minimize the time consumption and energy consumption of the system for processing structured tasks of terminal devices by jointly optimizing the offloading decisions, caching strategies, computation resource allocation and transmission power allocation. This problem is a mixed integer nonlinear programming problem that is nonconvex. In order to solve this challenging problem, proposed a multi-task multi-objective optimization algorithm (MO-MFEA-S) with adaptive knowledge migration based on MO-MFEA. The results of a large number of simulation experiments demonstrate the convergence and effectiveness of MO-MFEA-S.

查看原文本刊更多论文

基于 MO-MFEA 算法的智能交通场景下任务卸载和资源分配联合优化方案

随着交通数据的激增和服务的多样化，智能交通系统中用于数据处理的资源变得越来越有限。为了解决这一问题，本文研究了智能交通系统中采用边缘计算、NOMA 通信技术和边缘（内容）缓存技术的计算卸载和资源分配问题。目标是通过联合优化卸载决策、缓存策略、计算资源分配和传输功率分配，最大限度地减少系统处理终端设备结构化任务的时间消耗和能源消耗。这个问题是一个非凸的混合整数非线性编程问题。为了解决这个具有挑战性的问题，提出了一种基于 MO-MFEA 的自适应知识迁移的多任务多目标优化算法（MO-MFEA-S）。大量仿真实验结果证明了 MO-MFEA-S 的收敛性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Network and Computer Applications 工程技术-计算机：跨学科应用

CiteScore

21.50

自引率

3.40%

发文量

142

审稿时长

37 days

期刊介绍： The Journal of Network and Computer Applications welcomes research contributions, surveys, and notes in all areas relating to computer networks and applications thereof. Sample topics include new design techniques, interesting or novel applications, components or standards; computer networks with tools such as WWW; emerging standards for internet protocols; Wireless networks; Mobile Computing; emerging computing models such as cloud computing, grid computing; applications of networked systems for remote collaboration and telemedicine, etc. The journal is abstracted and indexed in Scopus, Engineering Index, Web of Science, Science Citation Index Expanded and INSPEC.