SentiLog: Anomaly Detecting on Parallel File Systems via Log-based Sentiment Analysis
SentiLog: Anomaly Detecting on Parallel File Systems via Log-based Sentiment Analysis
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DOI：
10.1145/3465332.3470873
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Di Zhang;Dong Dai;Runzhou Han;Mai Zheng
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文献类型：
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作者：
Di Zhang;Dong Dai;Runzhou Han;Mai Zheng
并行文件系统（pfs）作为高性能计算（HPC）平台的核心组件，其规模和复杂性迅速增长，容易发生各种故障和异常。在运行时识别异常对HPC操作人员和管理员非常有帮助。通过分析运行时日志来检测大型系统的异常已被许多研究证明是有效的。但是，由于pfse日志量大且不规则，将它们应用于并行文件系统日志面临着很大的挑战。本研究提出了SentiLog，一种分析PFSes系统日志以检测异常的新方法。与现有的解决方案不同，SentiLog的工作原理是基于从一组pfse中收集的与日志记录相关的源代码，训练一个一般的情感、自然语言模型。通过这种方式，SentiLog从源代码中学习开发人员嵌入的信息。初步结果表明，SentiLog能够准确预测异常，在两种典型pfse （Lustre和BeeGFS）上的表现优于最先进的测井分析解决方案。这一初步研究表明，情感分析可能是一种有前途的方法来分析复杂和不规则的系统日志。
As core components of High-performance computing (HPC) platforms, parallel file systems (PFSes) grow quickly in scale and complexity, hence are subject to various failures and anomalies. Identifying their anomalies in runtime is critically helpful for HPC operators and administrators. Analyzing the runtime logs to detect the anomalies of large-scale systems has been proven effective in many recent studies. However, applying them to parallel file systems logs faces significant challenges due to the large volume and irregularity of PFSes logs. This study proposes SentiLog, a new approach to analyzing PFSes system logs for detecting anomalies. Unlike existing solutions, SentiLog works by training a general sentimental, natural language model based on the logging-relevant source code collected from a set of PFSes. In this way, SentiLog learns information embedded by developers from the source code. Our preliminary results show SentiLog is able to accurately predict anomalies and performs better than state-of-the-art log analysis solutions on two representative PFSes (Lustre and BeeGFS). This preliminary study shows sentiment analysis could be a promising method to analyze complex and irregular system logs.