基于GRU循环神经网络的云数据中心应用故障预测方法  

Application failure prediction method of cloud data center based on GRU Recurrent Neural Network

作　　者：胡小宁 HU Xiaoning(China Railway Information Technology Group Co.Ltd.,Beijing 100844,China)

机构地区：[1]中国铁路信息科技集团有限公司,北京100844

出　　处：《铁路计算机应用》2022年第2期7-11,共5页Railway Computer Application

摘　　要：云数据中心的分布式应用故障具有复杂性、随机性等特点,导致应用的运行与维护(简称:运维)管理任务难度大、效率低。为此,提出一种云数据中心应用故障预测方法,构建基于门控循环单元(GRU,Gated Recurrent Unit)循环神经网络(RNN,Recurrent Neural Network)的云数据中心应用故障预测模型,对云数据中心的应用监控数据进行分析处理并预测将要出现的应用故障。试验结果表明,本方法预测精确率满足应用运维管理中故障提前发现和处理的相关要求,在降低应用运维管理难度和提升运维效率方面具有一定的实用价值。Distributed application failures in cloud data centers have the characteristics of complexity,randomness,which make applications operation and maintenance tasks difficult and inefficient.Therefore,this paper proposed an application failure prediction method for cloud data center,built an application fault prediction model of a cloud data center based on GRU(Gated Recurrent Unit) Recurrent Neural Network(RNN),analyzed and processed the application monitoring data of the cloud data center and predicted the application failures that will occur.The test results showed that the prediction accuracy of this method meets the relevant requirements of failure early detection and treatment in application operation and maintenance management,and has certain practical value in reducing the difficulty of application operation and maintenance management and improving operation and maintenance efficiency.

关 键 词：云数据中心 循环神经网络(RNN) 特征工程 门控循环单元(GRU) 故障预测 单层感知器(SLP) 

分 类 号：U29[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]