日志严重性级别分类：一种用于生产系统的方法
Log severity level classification: an approach for systems in production
摘要：
内容：对于系统开发人员和操作工程师来说，数据库通常是理解和诊断软件系统在生产中的行为的主要信息来源。在许多情况下，日志是可用于故障调查的唯一证据。问题：但是，日志严重性级别的选择不当可能会影响生成的日志数据量，从而影响质量。这种存储开销可能会影响基于日志的监控系统的性能，因为过多的日志数据会带来更大的聚合噪声，这使得在尝试进行诊断时利用实际重要的内容变得非常困难。目标：本研究旨在通过处理来自生产系统的日志数据的严重性级别来减少监控系统的开销。方法：为了实现这一目标，我们打算加深对日志严重性级别的了解，并开发一种日志严重性级别分类的自动化方法，证明减少日志严重性级别“噪音”可以提高对生产系统的监控。结论：我们希望这项工作的一系列贡献能够改进软件系统的监控活动，并有助于创建改进日志记录实践的知识
英文摘要：
Context: Logs are often the primary source of information for system developers and operations engineers to understand and diagnose the behavior of a software system in production. In many cases, logs are the only evidence available for fault investigation. Problem: However, the inappropriate choice of log severity level can impact the amount of log data generated and, consequently, quality. This storage overhead can impact the performance of log-based monitoring systems, as excess log data comes with increased aggregate noise, making it challenging to utilize what is actually important when trying to do diagnostics. Goal: This research aims to decrease the overheads of monitoring systems by processing the severity level of log data from systems in production. Approach: To achieve this goal, we intend to deepen the knowledge about the log severity levels and develop an automated approach to log severity level classification, demonstrating that reducing log severity level "noise" improves the monitoring of systems in production. Conclusion: We hope that the set of contributions from this work can improve the monitoring activities of software systems and contribute to the creation of knowledge that improves logging practices
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