PADLA: A Dynamic Log Level Adapter Using Online Phase Detection

摘要

Logging is an important feature for a software system to record its run-time information. Although detailed logs are helpful to identify the cause of a failure in a program execution, constantly recording detailed logs of a long-running system is challenging because of its performance overhead and storage cost. To solve the problem, we propose PADLA (Phase-Aware Dynamic Log Level Adapter) that dynamically adjusts the log level of a running system so that the system can record irregular events such as performance anomalies in detail while recording regular events concisely. PADLA is an extension of Apache Log4j, one of the most popular logging framework for Java. It employs an online phase detection algorithm to recognize irregular events. It monitors run-time performance of a system and learns regular execution phases of a program. If it recognizes a performance anomalies, it automatically changes the log level of a system to record the detailed behavior. In the case study, PADLA successfully recorded a detailed log for performance analysis of a server system under high load while suppressing the amount of log data and performance overhead.