基于BiLSTM神经网络的多服务器门限服务系统性能分析  

Performance Analysis of Multi-server Gated Service System Based on BiLSTM Neural Networks

作　　者：杨志军[1,2,3] 黄文洁 丁洪伟 YANG Zhijun;HUANG Wenjie;DING Hongwei(School of Information Science and Technology,Yunnan University,Kunming 650500,China;Educational Instruments and Facilities Service Center,Educational Department of Yunnan Province,Kunming 650223,China;Key Laboratory of Education Informatization for Nationalities of Ministry of Education,Yunnan Normal University,Kunming 650500,China)

机构地区：[1]云南大学信息学院,昆明650500 [2]云南省教育厅教学仪器装备中心,昆明650223 [3]云南师范大学教育部民族教育信息化重点实验室,昆明650500

出　　处：《计算机科学》2023年第10期266-274,共9页Computer Science

基　　金：国家自然科学基金(61461053,61461054);“云南省兴滇英才支持计划”产业创新人才专项(YNWR-CYJS-2020-017)。

摘　　要：为了满足运行速度快、时延低、性能好、公平性好等特点,提出了多服务器门限服务系统,并利用BiLSTM(Bi-direc-tional Long Short-Term Memory)神经网络对其进行预测分析,使用多服务器接入方式来降低网络时延,改善系统性能。多个服务器调度时,可以采用同步和异步两种方式。首先,研究多服务器门限服务的系统模型。其次,在单服务器的基础上,利用嵌入马尔可夫链和概率母函数的分析方法对多服务器门限服务的平均排队队长、平均循环周期和平均时延进行求解;同时,利用Matlab进行仿真实验,分别将单服务器系统与多服务器系统的理论值与仿真值进行系统分析,对比多服务器同步和异步两种方式。最后,构建BiLSTM神经网络来预测多服务器系统的性能。实验结果表明,该多服务器系统异步方式优于同步和单服务器系统,多服务器异步系统的性能更好,时延更低,效率更高。综合对比多服务器的3种基本服务系统,在保证公平性的情况下,门限服务系统更加稳定。并且使用BiLSTM神经网络预测算法能够准确预测系统的性能,提高计算效率,对轮询系统的性能评价具有指导意义。In order to meet the requirements of fast operation,low delay,good performance and fairness,a multi-server gated service system is proposed and its predictive analysis is carried out using BiLSTM(bi-directional long short-term memory)neural networks.Multi-server access is used to reduce the network delay and improve system performance.Both synchronous and asynchronous approaches can be used when multiple servers are scheduled.Firstly,the system model of multi-server gated service is investigated.Secondly,the average queue length,average cycle period and average delay of multi-server gated service are solved on the basis of single server using the analytical methods of embedded Markov chain and probabilistic generating function.Meanwhile,simulation experiments are conducted using Matlab to compare the theoretical and simulated values of single server system and multi-server system respectively system analysis,comparing both multi-server synchronous and asynchronous approaches.Finally,a BiLSTM neural network is constructed to predict the performance of the multi-server system.Experiments show that the asynchronous approach of this multi-server system is superior to the synchronous and the single-server system,and the multi-server asynchronous system has better performance,lower delay and higher efficiency.Comparing the three basic multi-server service systems,the gated service system is more stable while ensuring fairness.And the use of BiLSTM neural network prediction algorithm can accurately predict the performance of the system and improve the computational efficiency,which is a guideline for the performance evaluation of the polling system.

关 键 词：多服务器 同步方式 异步方式 平均排队队长 平均时延 公平性 BiLSTM神经网络 

分 类 号：TN911[电子电信—通信与信息系统]