TY - CHAP
T1 - Conclusion and Future Work
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 book, we introduce two critical application scenarios for SDN: SD-WANs and SD-DCNs. To improve network performance of SD-WANs and SD-DCNs, we leverage emerging ML techniques (i.e., DRL, MARL, and GNN) to maintain the load balance, increase the power efficiency, and improve the QoS. This book exhibits the effectiveness of ML for solving networking problems and paves the way for the future research on the usage of DRL, MARL, and GNN for computer networks.
AB - In this book, we introduce two critical application scenarios for SDN: SD-WANs and SD-DCNs. To improve network performance of SD-WANs and SD-DCNs, we leverage emerging ML techniques (i.e., DRL, MARL, and GNN) to maintain the load balance, increase the power efficiency, and improve the QoS. This book exhibits the effectiveness of ML for solving networking problems and paves the way for the future research on the usage of DRL, MARL, and GNN for computer networks.
UR - http://www.scopus.com/inward/record.url?scp=85139847882&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-4874-9_6
DO - 10.1007/978-981-19-4874-9_6
M3 - Chapter
AN - SCOPUS:85139847882
T3 - SpringerBriefs in Computer Science
SP - 67
EP - 68
BT - SpringerBriefs in Computer Science
PB - Springer
ER -