Open AccessAn intelligent routing optimization strategy based on deep reinforcement learning
Author(s) -
Xiong Zhou,
Hongyu Guo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012046
Subject(s) - reinforcement learning , computer science , routing (electronic design automation) , distributed computing , multipath routing , throughput , routing domain , static routing , computer network , artificial intelligence , routing protocol , telecommunications , wireless
Finding the optimal strategy in network routing has always been a NP hard problem. Due to the complexity and dynamics of network traffic, the existing intelligent routing algorithms have poor generalization ability. Therefore, this paper proposes an intelligent routing strategy based on deep reinforcement learning, and with the help of SDN control can dynamically collect network traffic distribution information, can dynamically adjust the routing strategy. Compared with traffic engineering algorithms such as TCMP and DRL-TE, the end-to-end delay is optimized under different throughput.