LSTM网络模型在Web服务器资源消耗预测中的应用研究

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.

Application of LSTM Network Model in Web Server Resource Consumption Prediction

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.