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Anomaly States Monitoring of Large-Scale Systems with Intellectual Analysis of System Logs

机译：利用系统日志的智能分析监控大型系统的异常状态

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The article analyzes the paths and algorithms for automating the monitoring of computer system states by means of intellectual analysis of unstructured system log data in order to detect and diagnose abnormal states. This information is necessary for technical support to locate the problem and diagnose it accurately. Because of the ever-growing log size, mining data mining models are used to help developers extract system information. At the first stage, logs are collected with records of system states and information on the execution of processes. At the second stage, the log parser is used to retrieve a group of event templates, with the result that the raw logs are structured. At the third stage, after the logs are parsed into separate patterns, they are additionally represented as numerical vectors of attributes (attributes). The set of all vectors forms a matrix of signs. In the fourth stage, the feature matrix is used to detect anomalies of machine learning methods to determine whether the new incoming log sequence is abnormal or not. A decision tree was used as a classification method for machine learning. Using the example of a distributed HDFS data set, the effectiveness of the considered method for detecting anomalous system states is shown.
机译：本文通过对非结构化系统日志数据进行智能分析，分析了用于自动监视计算机系统状态的路径和算法，以检测和诊断异常状态。此信息对于技术支持找到问题并准确诊断是必不可少的。由于日志大小不断增长，因此使用挖掘数据挖掘模型来帮助开发人员提取系统信息。在第一阶段，将收集日志，其中包含系统状态的记录以及有关流程执行的信息。在第二阶段，日志解析器用于检索一组事件模板，从而构成原始日志。在第三阶段，将日志解析为单独的模式后，它们会另外表示为属性（属性）的数字向量。所有向量的集合形成符号矩阵。在第四阶段，特征矩阵用于检测机器学习方法的异常，以确定新的传入日志序列是否异常。决策树被用作机器学习的分类方法。以分布式HDFS数据集为例，显示了所考虑的检测异常系统状态的方法的有效性。

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