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Machine Learning Empowered Intelligent Data Center Networking

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-19-7395-6

ISBN: 978-981-19-7394-9

Publisher: Springer Nature Singapore - Springer Journals

Oct 25, 2022

Available

Introduction

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

Pages: 1 –8

[As the storage and computation progressively migrate to the cloud, the data center (DC) as the core infrastructure of cloud computing provides vital technical and platform support for enterprise and cloud services. However, with the rapid rise of the data center scale, the network optimization,...
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Fundamentals of Machine Learning in Data Center Networks

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

Pages: 9 –14

[In this chapter, we will briefly review the common learning paradigms of ML and some preliminary knowledge about data collection and processing. Furthermore, to better assess the strengths and weaknesses of the existing research work, we design a multi-dimensional and multi-perspective quality...
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Machine Learning Empowered Intelligent Data Center Networking

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

Pages: 15 –99

[Machine learning has been widely studied and practiced in data center networks, and a large number of achievements have been made. In this chapter, we will review, compare, and discuss the existing work in the following research areas: flow prediction, flow classification, load balancing,...
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Insights, Challenges and Opportunities

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

Pages: 101 –108

[Through systematic research and analysis, we found that ML has been gradually introduced and applied to various fields of data center network, and has made certain achievements. However, the current researches are still in its infancy and need to be further improved in various areas.]
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Conclusion

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

Pages: 109 –109

[As the core infrastructure, data center provides a strong platform support for cloud computing, and so on. Nevertheless, the rapid growth of its network scale leads to great challenges in network optimization.]

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