智能化网格电信系统的故障预测方法

蔡珩; 戈磊

doi:10.11959/j.issn.1000-0801.2018118

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智能化网格电信系统的故障预测方法

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- 智能化网格电信系统的故障预测方法
- Intelligent fault prediction method of telecom system
- 电信科学 2018年34卷第6期页码：183-191
- 作者机构：
- 作者简介：
  
  [ "蔡珩（1976-），女，中国电信股份有限公司上海分公司工程师，主要研究方向为 IT智慧运营、利用大数据技术提升系统运维的智能化。" ]
  [ "戈磊（1973-），男，中国电信股份有限公司上海分公司企业信息化部高级项目经理，主要研究方向为云计算、开源架构、大数据分析、Devops运营、流程生命周期管控等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018118
  中图分类号： TP391.1
- 网络出版日期：2018-06，
  
  纸质出版日期：2018-06-20
- 稿件说明：
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蔡珩, 戈磊. 智能化网格电信系统的故障预测方法[J]. 电信科学, 2018,34(6):183-191.

Heng CAI, Lei GE. Intelligent fault prediction method of telecom system[J]. Telecommunications science, 2018, 34(6): 183-191.
蔡珩, 戈磊. 智能化网格电信系统的故障预测方法[J]. 电信科学, 2018,34(6):183-191. DOI： 10.11959/j.issn.1000-0801.2018118.

Heng CAI, Lei GE. Intelligent fault prediction method of telecom system[J]. Telecommunications science, 2018, 34(6): 183-191. DOI： 10.11959/j.issn.1000-0801.2018118.

摘要

尝试用基于深度学习的相关人工智能技术，分析服务器集群上的进程和端口网络，并对网络节点进行状态预测。具体地，结合运维过程中的先验知识对网络节点的特征进行细致选择，预测网络中各个进程和端口的异常（崩溃）状态。实验结果表明，进程节点的运行信息（如 CPU 和内存使用率）、进程间的通信情况以及进程节点在整个网络中的结构特征对于判断该节点的状态具有一定的指导价值，而这些特征在时间维度上的变化量同样反映了进程/端口的状态。

Abstract

Some approaches based on deep learning would be used to analyze the process and port network on a server cluster.Specifically

the features of nodes were carefully selected in server cluster network

by combining the prior knowledge from actual operations

and the abnormal state of processes or ports on the cluster was predicted.According to the research

the running information such as loads of CPU and memory

communications between processes and the structural features in the process network was valuable in predicting the states of processes and ports; furthermore

the changes of features mentioned above in the time dimension reflected the states of processes or ports

too.

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Keywords

references

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