Bo Li;Ting Wang;Peng Yang;Mingsong Chen;Mounir Hamdi
{"title":"重新思考数据中心网络:机器学习实现网络智能","authors":"Bo Li;Ting Wang;Peng Yang;Mingsong Chen;Mounir Hamdi","doi":"10.23919/JCIN.2022.9815199","DOIUrl":null,"url":null,"abstract":"To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. Machine learning (ML) provides an effective way to deal with these challenges by enabling network intelligence. To this end, numerous creative ML-based approaches have been put forward in recent years. Nevertheless, the intelligent optimization of data center networks (DCN) still faces enormous challenges. To the best of our knowledge, there is a lack of systematic and original investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we investigate the application of ML to DCN optimization and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, and resource management. Moreover, we also give unique insights into the technology evolution of the fusion of DCN and ML, together with some challenges and future research opportunities.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"7 2","pages":"157-169"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Rethinking Data Center Networks: Machine Learning Enables Network Intelligence\",\"authors\":\"Bo Li;Ting Wang;Peng Yang;Mingsong Chen;Mounir Hamdi\",\"doi\":\"10.23919/JCIN.2022.9815199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. Machine learning (ML) provides an effective way to deal with these challenges by enabling network intelligence. To this end, numerous creative ML-based approaches have been put forward in recent years. Nevertheless, the intelligent optimization of data center networks (DCN) still faces enormous challenges. To the best of our knowledge, there is a lack of systematic and original investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we investigate the application of ML to DCN optimization and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, and resource management. Moreover, we also give unique insights into the technology evolution of the fusion of DCN and ML, together with some challenges and future research opportunities.\",\"PeriodicalId\":100766,\"journal\":{\"name\":\"Journal of Communications and Information Networks\",\"volume\":\"7 2\",\"pages\":\"157-169\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Communications and Information Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/9815199/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications and Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/9815199/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rethinking Data Center Networks: Machine Learning Enables Network Intelligence
To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. Machine learning (ML) provides an effective way to deal with these challenges by enabling network intelligence. To this end, numerous creative ML-based approaches have been put forward in recent years. Nevertheless, the intelligent optimization of data center networks (DCN) still faces enormous challenges. To the best of our knowledge, there is a lack of systematic and original investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we investigate the application of ML to DCN optimization and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, and resource management. Moreover, we also give unique insights into the technology evolution of the fusion of DCN and ML, together with some challenges and future research opportunities.