Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components s...

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Autores principales:	Haiwei Dong, Ali Munir, Hanine Tout, Yashar Ganjali
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Data center network machine learning applications survey Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7a80b40ca6804054bd31b66b32c3b4cc
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spelling	oai:doaj.org-article:7a80b40ca6804054bd31b66b32c3b4cc2021-11-09T00:00:37ZNext-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities2169-353610.1109/ACCESS.2021.3117763https://doaj.org/article/7a80b40ca6804054bd31b66b32c3b4cc2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9559734/https://doaj.org/toc/2169-3536Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components such as servers and switches/routers that work together to achieve a common objective and serve these applications. Managing such large data centers is a tedious process and demands automation, intelligent control and decision making within the data center. Recently both the industry and academia have focused on bringing intelligence to the control, automation, and management of DCNs. Despite the variety of works that surveyed ML for networking, to the best of our knowledge, none has focused on DCN, which makes this survey original. Readers in the academic and industrial communities will all benefit from a comprehensive discussion of the ML solutions applied in DCN to address critical essential problems, including workload forecasting, traffic flow control, traffic classification and scheduling, topology management, network state prediction, root cause analysis, and network security. Furthermore, this article outlines the challenges and concludes with the future research venues in adopting ML for automatic, intelligent and autonomous DCNs.Haiwei DongAli MunirHanine ToutYashar GanjaliIEEEarticleData center networkmachine learning applicationssurveyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 136459-136475 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data center network machine learning applications survey Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Data center network machine learning applications survey Electrical engineering. Electronics. Nuclear engineering TK1-9971 Haiwei Dong Ali Munir Hanine Tout Yashar Ganjali Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
description	Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components such as servers and switches/routers that work together to achieve a common objective and serve these applications. Managing such large data centers is a tedious process and demands automation, intelligent control and decision making within the data center. Recently both the industry and academia have focused on bringing intelligence to the control, automation, and management of DCNs. Despite the variety of works that surveyed ML for networking, to the best of our knowledge, none has focused on DCN, which makes this survey original. Readers in the academic and industrial communities will all benefit from a comprehensive discussion of the ML solutions applied in DCN to address critical essential problems, including workload forecasting, traffic flow control, traffic classification and scheduling, topology management, network state prediction, root cause analysis, and network security. Furthermore, this article outlines the challenges and concludes with the future research venues in adopting ML for automatic, intelligent and autonomous DCNs.
format	article
author	Haiwei Dong Ali Munir Hanine Tout Yashar Ganjali
author_facet	Haiwei Dong Ali Munir Hanine Tout Yashar Ganjali
author_sort	Haiwei Dong
title	Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
title_short	Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
title_full	Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
title_fullStr	Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
title_full_unstemmed	Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities
title_sort	next-generation data center network enabled by machine learning: review, challenges, and opportunities
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/7a80b40ca6804054bd31b66b32c3b4cc
work_keys_str_mv	AT haiweidong nextgenerationdatacenternetworkenabledbymachinelearningreviewchallengesandopportunities AT alimunir nextgenerationdatacenternetworkenabledbymachinelearningreviewchallengesandopportunities AT haninetout nextgenerationdatacenternetworkenabledbymachinelearningreviewchallengesandopportunities AT yasharganjali nextgenerationdatacenternetworkenabledbymachinelearningreviewchallengesandopportunities
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Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

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