Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network

YANG Zhijun; MAO Lei; GAN Jianhou; DING Hongwei

doi:10.1049/cje.2020.09.005

Volume 29 Issue 6

Dec. 2020

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Article Navigation > Chinese Journal of Electronics > 2020 > 29(6): 1046-1053

YANG Zhijun, MAO Lei, GAN Jianhou, et al., “Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1046-1053, 2020, doi: 10.1049/cje.2020.09.005

Citation:

YANG Zhijun, MAO Lei, GAN Jianhou, et al., “Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1046-1053, 2020, doi: 10.1049/cje.2020.09.005

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Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network

doi: 10.1049/cje.2020.09.005

1.
School of Information Science and Technology, Yunnan University, Kunming 650500, China;
2.
Educational Instruments and Facilities Service Center, Educational Department of Yunnan Province, Kunming 650223, China;
3.
Key Laboratory of Education Informalization for Nationalities of Ministry of Education, Yunnan Normal University, Kunming 650500, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61461054, No.61461053 and No.61862068).

More Information

Corresponding author: GAN Jianhou (corresponding author) received the B.S. degree in computer science education and M.S. degree in mathematics from Yunnan Normal University, China, in 1998 and 2004. He received Ph.D. degree in computational metallurgy from Kunming University of Science and Technology, China, in 2016. He is the vice director of Key Laboratory of Educational Information for Nationalities, Yunnan Normal University, Kunming, P.R. China. His current research interests include knowledge engineering and educational information for nationalities.(Email:ganjh@ynu.edu.cn)
Received Date: 2019-10-11
Publish Date: 2020-12-25

Abstract

Abstract

To solve the poor performance of the single-server polling system in high traffic and the complex analysis of the multi-server polling system, a synchronous double-server polling system is proposed, and its performance is analyzed using a Backpropagation (BP) neural network prediction algorithm. Experimental data are processed and analyzed, and a three-layer multiinput single-output BP network model is constructed to predict the performance of the polling system under different arrival rates of information packets. In the prediction stage, first, the data are processed and the average queue length under different information arrival rates is used to form a sequence. Subsequently, a multiinput single-output BP neural network is constructed for prediction. Experimental results show that the algorithm can accurately predict the performance of the double-server polling system, thereby facilitating research regarding polling systems.
- Polling system,
- BP neural network,
- Prediction,
- Average queue length,
- Long-short term memory (LSTM)

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References(20)

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