边缘计算的成本优化:调查

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2024-10-01 DOI:10.1007/s10462-024-10947-4
Liming Cao, Tao Huo, Shaobo Li, Xingxing Zhang, Yanchi Chen, Guangzheng Lin, Fengbin Wu, Yihong Ling, Yaxin Zhou, Qun Xie
{"title":"边缘计算的成本优化:调查","authors":"Liming Cao,&nbsp;Tao Huo,&nbsp;Shaobo Li,&nbsp;Xingxing Zhang,&nbsp;Yanchi Chen,&nbsp;Guangzheng Lin,&nbsp;Fengbin Wu,&nbsp;Yihong Ling,&nbsp;Yaxin Zhou,&nbsp;Qun Xie","doi":"10.1007/s10462-024-10947-4","DOIUrl":null,"url":null,"abstract":"<div><p>The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 11","pages":""},"PeriodicalIF":10.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10947-4.pdf","citationCount":"0","resultStr":"{\"title\":\"Cost optimization in edge computing: a survey\",\"authors\":\"Liming Cao,&nbsp;Tao Huo,&nbsp;Shaobo Li,&nbsp;Xingxing Zhang,&nbsp;Yanchi Chen,&nbsp;Guangzheng Lin,&nbsp;Fengbin Wu,&nbsp;Yihong Ling,&nbsp;Yaxin Zhou,&nbsp;Qun Xie\",\"doi\":\"10.1007/s10462-024-10947-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.</p></div>\",\"PeriodicalId\":8449,\"journal\":{\"name\":\"Artificial Intelligence Review\",\"volume\":\"57 11\",\"pages\":\"\"},\"PeriodicalIF\":10.7000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10462-024-10947-4.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10462-024-10947-4\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-024-10947-4","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

由于无线通信技术的广泛应用和计算密集型移动应用需求的不断增长,边缘计算模式正日益商业化。边缘计算是对云计算模式的补充,它将计算、存储和网络资源部署到无线接入网络的边缘位置,使终端设备能够运行资源密集型应用。为了促进边缘计算的商业化,探索降低边缘计算网络成本的有效方法非常重要。本文全面回顾了近年来的研究成果,为研究动态提供了清晰的视角。本文首先回顾了边缘计算的架构框架。然后,全面阐述了主要优化目标和优化方法。然后,深入展示了降低成本的主流数学模型。本文还讨论了用于评估有效性的方法。然后,深入分析了边缘计算网络的典型应用场景。最后,本文指出了一些尚未解决的问题。我们期待未来的研究在这些方向上做出更多尝试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Cost optimization in edge computing: a survey

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
期刊最新文献
Federated learning design and functional models: survey A systematic literature review of recent advances on context-aware recommender systems Escape: an optimization method based on crowd evacuation behaviors A multi-strategy boosted bald eagle search algorithm for global optimization and constrained engineering problems: case study on MLP classification problems Innovative solution suggestions for financing electric vehicle charging infrastructure investments with a novel artificial intelligence-based fuzzy decision-making modelling
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1