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.

引用

页码：2170 / 2177

页数：7

共 29 条

[1]

Egilmez H E, Dane S T, Bagci K T, Et al., OpenQoS: An OpenFlow controller design for multimedia delivery with end-to-end quality of service over software-defined networks, Signal & Information Processing Association Summit and Conference, pp. 1-8, (2012)

[2]

Liu J, Shroff N B, Xia C H, Et al., Joint congestion control and routing optimization: An efficient second-order distributed approach, IEEE/ACM Transactions on Networking, 24, 3, pp. 1-17, (2015)

[3]

Clark D D, Partridge C, Ramming J C, Et al., A knowledge plane for the internet, Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 68-73, (2003)

[4]

Thomas R W, Dasilva L A, Mackenzie A B., Cognitive networks, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 11-50, (2005)

[5]

Derbel H, Agoulmine N, Salaun M., ANEMA: Autonomic network management architecture to support self-configuration and self-optimization in IP networks, Computer Networks, 53, 3, pp. 418-430, (2009)

[6]

Zorzi M, Zanella A, Testolin A, Et al., COBANETS: A new paradigm for cognitive communications systems, International Conference on Computing, (2016)

[7]

Mestres A, Rodrigueznatal A, Carner J, Et al., Knowledge-defined networking, ACM Sigcomm Computer Communication Review, 47, 3, pp. 2-10, (2016)

[8]

Agoulmine N, Balasubramaniam S, Botvitch D, Et al., Challenges for autonomic network management, The 1st IEEE International Workshop on Modelling Autonomic Communications Environments (MACE), pp. 87-92, (2006)

[9]

McKeown Nick, Anderson Tom, Balakrishnan Hari, Et al., OpenFlow: Enabling innovation in campus networks, ACM SIGCOMM Computer Communication Review, 38, 2, pp. 69-74, (2008)

[10]

Sivaraman A, Kim C, Krishnamoorthy R, Et al., DC.p4: Programming the forwarding plane of a data-center switch, ACM SIGCOMM, pp. 1-8, (2016)

← 1 2 3 →