EPAS: Efficient Online Log Parsing via Asynchronous Scheduling of LLM Queries

System logs are critical for understanding the runtime behavior of information systems, yet their semi-structured nature poses challenges for effective utilization. Log parsing addresses this by transforming logs into structured representations, facilitating downstream tasks such as anomaly detection and failure analysis. Traditional approaches relying on statistical features or deep learning struggle with semantic understanding, efficiency, and adaptability to unseen data. Large language models (LLMs) offer new opportunities with their advanced semantic capabilities, yet current LLM-based log parsing methods are limited by sequential processing inefficiencies, suboptimal sampling strategies, and lack of robust template refinement mechanisms. To address these challenges, we propose EPAS (Efficient Parsing via Asynchronous Scheduling), a novel parser that utilizes asynchronous scheduling to optimize LLM- based log parsing. EPAS introduces three key innovations: a dynamic asynchronous mechanism to decouple LLM-based parsing from scheduling, maximizing efficiency; a controversy-based sampling mechanism to provide examples that help accurately parse challenging words; and an LLM-based validation task to semantically refine templates with minimal overhead. Experiments on benchmark datasets demonstrate that EPAS significantly outperforms state-of-the-art methods, achieving over a 360% improvement in parsing efficiency while maintaining high accuracy. In addition, EPAS achieves the best average performance across all four metrics, highlighting its effectiveness in both efficiency and accuracy.¹