摘要
为进一步提升云监控平台数据分析的准确性和效率,在研究了集成云平台和分布式计算基础上,设计了融合机器学习的云监控数据分析平台。该平台包含核心结构和扩展结构2部分,分别用于对小量数据及大规模数据进行分析及分布式计算。核心架构可分为3层:智能搜索层、应用程序接口(API)层和云计算层。这3层相互作用,处理工作负载跟踪,并监视任何异常模式。扩展结构有利于用户在Hadoop文件系统中存储数据,且为用户提供了高可用性和可扩展的存储功能。对核心结构及扩展结构进行仿真分析。结果进一步验证了云监控数据分析平台的有效性及实用性。该平台为系统CPU及内存使用分析过程提供了一定依据。
In order to further improve the accuracy and efficiency of data analysis of cloud monitoring platform,a cloud monitoring data analysis platform integrating machine learning is designed based on the research of integrated cloud platform and distributed computing.The platform consists of the core structure and the extended structure,which can be used to analyze and distribute small data and large data respectively.The core architecture can be divided into three layers,intelligent search layer,application programming interface layer(API)and cloud computing layer.These three layers interact to handle workload tracking and monitor for any exception patterns.The extended structure is beneficial for users to store data in Hadoop file system,and to provide users with high availability and scalable storage function.The core structure and extended structure are simulated and analyzed,and the results further verify the effectiveness and practicability of the cloud monitoring data analysis platform.The platform provides a basis for the analysis of CPU and memory usage.
作者
余少锋
佘俊
钟建栩
廖崇阳
YU Shaofeng;SHE Jun;ZHONG Jianxu;LIAO Chongyang(Information and Communication Branch,China Southern Power Grid Peak and Frequency Modulation Power Generation Co. ,Ltd. ,Guangzhou 510000,China)
出处
《自动化仪表》
CAS
2022年第3期75-78,共4页
Process Automation Instrumentation
关键词
云监控
分布式
数据分析
机器学习
负载预测
云计算
异常监视
智能搜索
Cloud monitoring
Distributed
Data analysis
Machine learning
Load forecasting
Cloud computing
Abnormal monitoring
Intelligent search
