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

机译：异常国家对系统日志智力分析的大规模系统的监测

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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的智力分析来分析用于自动监测计算机系统状态的路径和算法，以便检测和诊断异常状态。这些信息是技术支持所必需的，以便准确定位问题并诊断它。由于日志大小不断增长，挖掘数据挖掘模型用于帮助开发人员提取系统信息。在第一阶段，使用系统状态的记录和关于执行进程的信息收集日志。在第二阶段，日志解析器用于检索一组事件模板，结果是原始日志是结构的。在第三阶段，将日志解析为单独的模式后，它们另外表示为属性的数值v（属性）。所有载体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。使用分布式HDFS数据集的示例，示出了考虑方法检测异常系统状态的有效性。

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