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基于表示学习的告警数据流压缩算法OA北大核心CSTPCD

ALERT STREAM COMPRESSION METHOD BASED ON REPRESENTATION LEARNING

中文摘要英文摘要

大型在线服务系统的告警数量巨大且关联关系复杂,运维人员进行故障诊断的难度较大.为此,提出一种基于表示学习的告警数据流压缩算法.该算法包含离线学习和在线压缩阶段:离线学习阶段,采用嵌入技术对告警内容的语义信息及服务组件的拓扑信息进行表示学习;在线压缩阶段,采用流式聚类方法对表示学习得到的告警向量进行聚合并生成告警事件.在合成数据集与真实数据集上的实验表明,该算法的各项评价指标均优于已有算法,更能满足告警数据流压缩的实时性和有效性要求.

The large-scale online service system has a large number of alerts,and the correlations of which are rather complicated,which greatly increases the difficulty of fault diagnosis for operators.To solve this problem,we propose an alert stream compression method based on representation learning.The method included two stages:offline learning stage and online compression stage.In the offline learning stage,the semantic information of the original alert data and the topology information between components were learned and represented through embedding technologies.In the online compression stage,the streaming clustering method was used to associate the alert vectors by representation learning in real-time.Experiments on the synthetic dataset and the real dataset show that the method can meet the real-time and effectiveness requirements of the alert stream compression.

阴振生;陈佳;王鹏;汪卫

复旦大学计算机科学技术学院 上海 200438

计算机与自动化

在线服务系统告警数据流压缩表示学习词嵌入图嵌入流式聚类

Online service systemAlert stream compressionRepresentation learningWord embeddingGraph embeddingStreaming clustering

《计算机应用与软件》 2024 (007)

34-41 / 8

10.3969/j.issn.1000-386x.2024.07.006

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