An Intelligent Routing Technology Based on Deep Reinforcement Learning

被引：0
|
作者：
Sun P.-H. [1 ]
Lan J.-L. [1 ]
Shen J. [1 ]
Hu Y.-X. [1 ]
机构：
[1] PLA Strategic Support Force Information Engineering University, Zhengzhou
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 11期
关键词：
Artificial intelligence (AI); Deep reinforcement learning (DRL); Routing optimization; Software-defined networking (SDN);
D O I：
10.3969/j.issn.0372-2112.2020.11.011
中图分类号：
学科分类号：
摘要：
With the expansion of network scale and network complexity, traditional routing algorithms cannot ensure both the calculation complexity and performance under the large fluctuation of spatial-temporal distribution of network traffic.In recent years, with the development of Software-Defined Networking (SDN) and Artificial Intelligence (AI), AI-based methods of automatic routing strategies are gaining attention.In this paper, we propose an intelligent network routing technology called SmartPath based on Deep Reinforcement Learning (DRL).With dynamic collection of network status, we can use DRL to generate routing policies automatically, thus ensuring that the routing policy can dynamically adapt to the change of network traffic.Experiment result shows that the proposed scheme can adjust the routing strategy dynamically without human experience on traffic analysis and can reduce the average end-to-end transmission delay by at least 10% compared with the state-of-art schemes. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：2170 / 2177
页数：7
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