高能物理科学数据中心智能运维系统

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

【目的】高能物理科学数据中心运维环境复杂,监控工具种类繁多,功能相对重叠且监控数据无法互通,日常运维面临巨大的挑战。为高效运用监控数据,提高数据中心运维能力,本文实现了高能物理科学数据中心智能运维系统。【方法】本文结合工业大数据技术、机器学习技术和数据中心运维需求,设计了通用的数据中心运维技术架构。介绍监控数据采集、分析、存储、共享、可视化等系统核心功能及其实现方式,以及依托该系统在数据中心数据存储、计算服务、网络安全等日常运维的具体应用效果。【结果】本文设计的运维框架,在高能物理科学数据中心日常运维中得到了成熟的应用和实践,提升了数据中心运维管理能力。【结论】智能运维系统在高能物理科学数据中心的应用,加速了运维监控从数据持久化、统一化到数据业务化、生态化的价值演进,实现了基于数据驱动的数据中心智能化运维生态。

[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.