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Received:December 10, 2018 Revised:January 11, 2019
Received:December 10, 2018 Revised:January 11, 2019
中文摘要: 如何能够准确地对软件老化趋势进行预测，并及时采取相应恢复策略是当前预防软件老化的一个关键问题.为此，针对老化数据的时序特性，以循环神经网络（Recurrent Neural Network，RNN）及其变种长短时记忆单元（Long Short-Term Memory，LSTM）结构为基础，设计了一种基于LSTM网络的软件老化资源预测方法，并通过应用加速寿命测试实验搭建老化测试平台，对Web服务器因内存泄漏引起的老化现象进行建模和预测.实验结果表明，LSTM老化预测模型在处理Web软件老化的时间序列建模问题上，具有很强的适用性和更高的准确性，能有效提高软件系统的可靠性和可用性.
Abstract:How to predict the aging trend of the software accurately, and take the corresponding recovery strategy is a key problem of preventing software aging. To solve the problem, this study designs a resource prediction method based on Recurrent Neural Network (RNN) and its variant-Long Short-Term Memory (LSTM), and builds an accelerated aging test platform to model and forecast the aging phenomenon of the Web server due to memory leak. The experiments show that LSTM network prediction model proves to be superior to the other traditional models in dealing with the time sequence modeling of aging parameters, with the predicted results closer to the actual values and the higher prediction accuracy, which can effectively improve the reliability and availability of the software system.
keywords: software aging LSTM resource consumption Recurrent Neural Network (RNN) software reliability
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基金项目:山西省中科院科技合作项目（20141101001）；山西省重点研发计划（一般）工业项目（201703D121042-1）；山西省社会发展科技项目（20140313020-1）
引用文本：
谭宇宁,党伟超,白尚旺,潘理虎.LSTM网络模型在Web服务器资源消耗预测中的应用研究.计算机系统应用,2019,28(7):214-220
TAN Yu-Ning,DANG Wei-Chao,BAI Shang-Wang,PAN Li-Hu.Application of LSTM Network Model in Web Server Resource Consumption Prediction.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):214-220
谭宇宁,党伟超,白尚旺,潘理虎.LSTM网络模型在Web服务器资源消耗预测中的应用研究.计算机系统应用,2019,28(7):214-220
TAN Yu-Ning,DANG Wei-Chao,BAI Shang-Wang,PAN Li-Hu.Application of LSTM Network Model in Web Server Resource Consumption Prediction.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):214-220
