海量监视数据云存储服务模型的设计与实现

Design and Implementation of Massive Surveillance Data Cloud Storage Service Model

  • 摘要: 集约化监控有助于全面把控业务信息系统运行情况与提升运维效率,已成为大型企事业部门业务监控的发展方向,但集约化监控带来海量监视数据的存储与服务挑战。以气象部门业务监视数据为例,在分析监视数据的特点及对比海量数据存储服务技术的基础上,设计和实现了海量监视数据云存储服务模型,主要通过时序数据存储技术和索引存储技术分别存储指标类和日志事件类监视数据,并实现存储优化。对该模型的应用效果测试分析表明, 所提海量监视数据云存储服务模型具有高效存储服务性能以及可扩展性稳定的特点。

     

    Abstract: Intensive monitoring is helpful to control the operational status of information systems and improv the efficiency of operation and maintenance, which has become the development direction of business monitoring of large-scale enterprises and departments. However, intensive monitoring brings about challenges in processing storage services for massive surveillance data. Taking an application in meteorological department business monitoring as an example, on the basis of analyzing the characteristics of monitoring data and comparing the technologies of massive data storage services, a massive surveillance data cloud storage service model is designed and implemented, it is capable of storing Indicator class and log event class monitoring data mainly through time series data storage and index storage technology, and able to achieve storage optimization. Application test results show that this massive surveillance data cloud storage service model has high efficiency, scalability and stability.

     

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