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Optimal path strategy for the web computing under deep reinforcement learning

Mu Shengdong (Collaborative Innovation Center for Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing, China and Chongqing Vocational College of Transportation, Yongchuan, Chongqing, China)
Wang Fengyu (Collaborative Innovation Center for Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing, China)
Xiong Zhengxian (Collaborative Innovation Center for Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing, China)
Zhuang Xiao (School of Management and Economics, Chongqing University of Arts and Sciences, Yongchuan, China)
Zhang Lunfeng (School of Electronic Information Engineering, Yangtze Normal University, Chongqing, China)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 29 October 2020

Issue publication date: 9 November 2020

102

Abstract

Purpose

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

Design/methodology/approach

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Findings

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

Originality/value

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

Keywords

Citation

Shengdong, M., Fengyu, W., Zhengxian, X., Xiao, Z. and Lunfeng, Z. (2020), "Optimal path strategy for the web computing under deep reinforcement learning", International Journal of Web Information Systems, Vol. 16 No. 5, pp. 529-544. https://doi.org/10.1108/IJWIS-08-2020-0055

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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