Intelligent Operation and Maintenance System for High Energy Physics Science Data Center

doi:10.11871/jfdc.issn.2096-742X.2022.01.003

Abstract

Abstract:

[Objective] The High-energy Physical Science Data Center has a complex operation and maintenance environment. Because the monitoring tools are various, the functions are relatively overlapped, and the monitoring data cannot be interoperable, the daily operation and maintenance are facing many challenges. To make full use of the monitoring data and improve the operation and maintenance capabilities of the data center, this paper implements an intelligent operation and maintenance system for the high-energy physical science data center. [Methods] This article combines industrial big data technology, machine learning technology, and data center operation and maintenance requirements to design a general data center operation and maintenance technology architecture. It introduces the core functions of the monitoring data collection, analysis, storage, sharing, visualization, etc., and their implementation methods. The application effects of this system in the direction of data center data storage, computing services, and network security operation and maintenance are also introduced. [Results] The operation and maintenance framework designed in this paper has been maturely applied and practiced in the daily operation and maintenance of the High-energy Physical Science Data Center and has improved the data center operation and maintenance management capabilities. [Conclusions] The application of intelligent operation and maintenance systems in the High-energy Physical Science Data Center has enhanced the value of operation and maintenance data and realized the data-driven intelligent operation and maintenance ecology of data centers.

Key words: big data, data center operation and maintenance, intelligent operation and maintenance system

HU Qingbao,ZHENG Wei,WANG Jiarong,WANG Lu,YAN Tian. Intelligent Operation and Maintenance System for High Energy Physics Science Data Center[J]. Frontiers of Data and Computing, 2022, 4(1): 30-41.

Figures/Tables 8

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References 11

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数据集	机器学习算法	应用
DNS日志	孤立森林与LOF算法	异常IP检测
DNS日志	SVM与CNN算法	恶意域名检测
网络连接日志	KNN、CNN+LSTM、随机森林算法	入侵检测
网络连接日志	有向图算法	内网横向移动检测
SSH登录日志	LSTM算法	异常登录检测

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