基于表示学习的告警数据流压缩算法
ALERT STREAM COMPRESSION METHOD BASED ON REPRESENTATION LEARNING
阴振生 1陈佳 1王鹏 1汪卫1
作者信息
- 1. 复旦大学计算机科学技术学院 上海 200438
- 折叠
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.
Abstract
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.
关键词
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Key words
Online service system/Alert stream compression/Representation learning/Word embedding/Graph embedding/Streaming clustering引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据