{"title":"Resource Provisioning and Utilization in 5G Network Slicing: A Survey of Recent Advances, Challenges, and Open Issues","authors":"S. Asakipaam, J. J. Kponyo, K. Gyasi","doi":"10.22247/ijcna/2023/220736","DOIUrl":null,"url":null,"abstract":"– The increasing demands for higher bandwidth and lower latency in modern telecommunications networks have led to the exploration of network slicing as a means to meet these requirements more efficiently in next-generation 5G networks. Despite substantial academic interest in resource allocation and management in network slicing, existing research is dispersed and fragmented. This study presents a categorization and assessment of the latest research on resource allocation and optimization techniques in 5G network slicing. It also shows how advanced machine learning techniques can support resource management in sliced wireless networks. The present paper offers a complete overview and analysis of current solutions for resource allocation and management in 5G network slicing, outlines open research challenges, and suggests future research directions for researchers and engineers in this field","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Networks and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22247/ijcna/2023/220736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
– The increasing demands for higher bandwidth and lower latency in modern telecommunications networks have led to the exploration of network slicing as a means to meet these requirements more efficiently in next-generation 5G networks. Despite substantial academic interest in resource allocation and management in network slicing, existing research is dispersed and fragmented. This study presents a categorization and assessment of the latest research on resource allocation and optimization techniques in 5G network slicing. It also shows how advanced machine learning techniques can support resource management in sliced wireless networks. The present paper offers a complete overview and analysis of current solutions for resource allocation and management in 5G network slicing, outlines open research challenges, and suggests future research directions for researchers and engineers in this field