面向云原生的智能运维架构和关键技术

doi:10.11959/j.issn.1000-0801.2020320

Abstract

Abstract:

Relying on the advantages of simple and fast applications,easy deployment of applications,and on-demand scaling of running applications,cloud native technology has been rapidly promoted.However,with the application of cloud native,the system architecture has become more complex and the scale of resources has become larger,and there is an urgent need for more intelligent operation and maintenance.Using intelligent operation and maintenance can better assist cloud-native application development,reduce operation and maintenance costs,and improve service quality.The application scenarios,key points,classic practices and existing problems of intelligent operation and maintenance were analyzed,and a cloud-native intelligent operation and maintenance architecture were proposed,and the main principles of the three scenarios:the trend prediction,data anomaly detection and fault location diagnosing in the core service algorithm platform were introduced in detail,and finally the future of intelligent operation and maintenance were looked forward.

Key words: cloud native, AIOps, micro service, container

CLC Number:

TP309

Long XUE,Gang LU,Qi ZHOU,Huiyan ZHANG,Tingjun WAN. Cloud native intelligent operation and maintenance technology[J]. Telecommunications Science, 2020, 36(12): 105-112.

Figures/Tables 4

References 12

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场景	算法	说明
异常检测	Isolation Forest	孤立森林
	Logistic Regression	逻辑回归
	DNN (deep neural network)	深度神经网算法
	RNN (recurrent neural network)	循环神经网络
	LSTM (long short-term memory)	长短期记忆网络
容量预测	HoltWinters/Arima/ETS	趋势预测
	Random Forest	随机森林
	DNN (deep neural network)	深度神经网算法
	LSTM (long short-term memory)	长短期记忆网络
	SVM (support vector machine)	支持向量机
智能问答	LSTM (long short-term memory)	长短期记忆网络
	TF-IDF	词频-逆文件频率
	Word2vec	自然语言处理
故障诊断	ID3/C4.5/CART	决策树
	MCTS	蒙特卡洛树搜索
指标间关联分析	Pearson/Spearman/Kendall Correlation	相关性分析
事件关联挖掘	FPGrowth	关联规则挖掘
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Cloud native intelligent operation and maintenance technology

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