{"title":"Cost optimization in edge computing: a survey","authors":"Liming Cao, Tao Huo, Shaobo Li, Xingxing Zhang, Yanchi Chen, Guangzheng Lin, Fengbin Wu, Yihong Ling, Yaxin Zhou, 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}
引用次数: 0
Abstract
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.
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.