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论文摘要

基于LSTM的WEB服务响应时间大数据预测方法

Big data prediction method of WEB service response time based on LSTM

作者:刘承启(南昌大学网络中心);林振荣(南昌大学信息工程学院);黄文海(南昌大学网络中心)

Author:LIU Cheng-Qi(Center of Network, Nanchang University);LIN Zhen-Rong(School of Information Engineering, Nanchang University);HUANG Wen-Hai(Center of Network, Nanchang University)

收稿日期:2018-10-26          年卷(期)页码:2019,56(1):71-77

期刊名称:四川大学学报: 自然科学版

Journal Name:Journal of Sichuan University (Natural Science Edition)

关键字:响应时间;LSTM;特征降维;

Key words:Response time; LSTM; Feature dimension reduction;

基金项目:江西省科技支撑计划项目(20151BBE50057); 江西省教育厅科技项目(GJJ161675,GJJ161675)

中文摘要

有效地预测Web服务器响应时间,对Web服务提供方保障服务质量有着重要的指导意义.利用大数据方法对处理大量历史指标数据的处理能提高预测的效率.本文提出一种使用相关性分析除去与响应时间相关性不高的指标项,使用特征降维的方法减小计算的数据量,使用动态调节参数的多层LSTM优化算法对数据做训练并预测响应时间的方法来提高预测的效率和准确率.通过实验证明,本文提出的方法能高效和准确地预测Web服务响应时间.

英文摘要

Effective prediction of web services response time has important guiding significance for service providers to guarantee the quality of service, using big data approach to process a large number of indictors’ historical data can improve the efficiency of prediction. Correlation analysis is proposed to remove indicators’ items that are not highly correlated with response time, the computed data volume is reduced by the feature dimension reduction, and the multi layer LSTM optimization algorithm with dynamic adjustment parameters is designed to predict the response time of web services. Experiments show that the proposed method can predict the response time of Web services efficiently and accurately.

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