Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs

Abstract

Log-based anomaly detection has been extensively studied to help detect complex runtime anomalies in production systems. However, existing techniques exhibit several common issues. First, they rely heavily on expert-labeled logs to discern anomalous behavior patterns. But labelling enough log data manually to effectively train deep neural networks may take too long. Second, they rely on numeric model prediction based on numeric vector input which causes model decisions to be largely non-interpretable by humans which further rules out targeted error correction. In recent years, we have witnessed groundbreaking advancements in large language models (LLMs) such as ChatGPT. These models have proven their ability to retain context and formulate insightful responses over entire conversations. They also present the ability to conduct few-shot and in-context learning with reasoning ability. In light of these abilities, it is only natural to explore their applicability in understanding log content and conducting anomaly classification among parallel file system logs.