计算机与现代化 ›› 2012, Vol. 1 ›› Issue (6): 191-194,.doi: 10.3969/j.issn.1006-2475.2012.06.051

• 应用与开发 • 上一篇    下一篇

一种基于Hadoop的云运维监控模型设计与实现

张 建1,2,耿焕同1,路有兵1,2   

  1. 1.南京信息工程大学江苏省网络监控中心,江苏 南京 210044;2.南京信息工程大学计算机与软件学院,江苏 南京 210044
  • 收稿日期:2012-03-16 修回日期:1900-01-01 出版日期:2012-06-14 发布日期:2012-06-14

Design and Implementation of Cloud Operation and Maintenance Monitoring Model Based on Hadoop

ZHANG Jian1,2, GENG Huan-tong1, LU You-bing1,2   

  1. 1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China;2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Received:2012-03-16 Revised:1900-01-01 Online:2012-06-14 Published:2012-06-14

摘要: 随着企业信息化在生产实时监测、海量存储和科学分析决策等方面的需求不断提升,运维监控系统已逐渐成为主要的管理手段。采用最新的云计算技术,设计及搭建一个数据规模易扩展、处理速度快、安全性高、成本低的云运维监控系统;针对运维控制系统中海量监控历史数据实时提取响应速度慢的缺点,设计并实现一种基于Hadoop的分布式海量数据处理模型。仿真实验证明,Hadoop在对云监控系统中的海量数据提取效率优于传统方法,随着数据量的快速增长,优势越明显。

关键词: 云计算, Hadoop, 分布式计算, 运维监控

Abstract: With the rising of enterprise informatization demands in the production of real-time monitoring, massive storage and scientific analysis and decision, the operation and maintenance monitoring systems have gradually become the main management tools. By using the latest cloud computing technology, this paper designs and builds a cloud operation and maintenance monitoring system, which is easy to expand for data scale, quick for processing speed, high for security, and low for cost. And in the light of the shortcomings of slow response to the real-time extraction of massive and historical monitoring data in operation and maintenance control system, the paper designs and implements a distributed massive data processing model based on Hadoop. Simulation experiments show that the massive data extraction efficiency of the cloud monitoring system based on Hadoop is superior to traditional methods, and the advantage is more obvious with the rapid growth of the amount of data.

Key words: cloud computing, Hadoop, distributed computing, operation and maintenance monitoring

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