TY - CHAP
T1 - Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs
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 ScaleDRL, which combines the control theory and DRL to achieve an efficient network control scheme for Traffic Engineering (TE). ScaleDRL employs the pinning control to select a subset of links in the network as critical links and uses a DRL algorithm to dynamically adjust link weights of the critical links. Thus, the dynamic link weight adjustment coupled with the weighted shortest path algorithm enables dynamic adjust most of the forwarding paths of flows.
AB - In this chapter, we introduce ScaleDRL, which combines the control theory and DRL to achieve an efficient network control scheme for Traffic Engineering (TE). ScaleDRL employs the pinning control to select a subset of links in the network as critical links and uses a DRL algorithm to dynamically adjust link weights of the critical links. Thus, the dynamic link weight adjustment coupled with the weighted shortest path algorithm enables dynamic adjust most of the forwarding paths of flows.
UR - https://www.scopus.com/pages/publications/85139822493
U2 - 10.1007/978-981-19-4874-9_2
DO - 10.1007/978-981-19-4874-9_2
M3 - Chapter
AN - SCOPUS:85139822493
T3 - SpringerBriefs in Computer Science
SP - 7
EP - 22
BT - SpringerBriefs in Computer Science
PB - Springer
ER -