TY - CHAP
T1 - Machine Learning for Software-Defined Networking
AU - Guo, Zehua
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.
AB - In this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.
UR - http://www.scopus.com/inward/record.url?scp=85139866792&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-4874-9_1
DO - 10.1007/978-981-19-4874-9_1
M3 - Chapter
AN - SCOPUS:85139866792
T3 - SpringerBriefs in Computer Science
SP - 1
EP - 6
BT - SpringerBriefs in Computer Science
PB - Springer
ER -