Log parsing, as a process of extracting structured information from semi-structured raw log data, is a crucial step in log analysis workflows. Rule-based parsing methods often overlook the rich semantic information contained in logs. Recently, LLM-based methods face three major challenges: 1) limited understanding of domain-specific logs due to lack of professional domain knowledge; 2) significant parsing costs and potential data privacy risks associated with commercial LLMs like ChatGPT; and 3) the Group Accuracy (GA) is highly susceptible to individual anomalous data. To address these challenges, we propose LogExpertSolver, a multi-agent framework for domain-specialized log parsing. This framework effectively reduces manual intervention in log parsing through inter-agent collaboration mechanisms. LogExpertSolver employs locally deployed medium-scale language models to construct multiple agents with diverse domain expertise. By decomposing complex log parsing tasks into a series of fine-grained subtasks, which are handled by corresponding expert agents, the framework achieves efficient log parsing. Through evaluation on the LogHub2.0 dataset, LogExpertSolver achieves an average Parsing Accuracy (PA) of 0.915, surpassing state-of-the-art parsers (LibreLog and LILAC) by 6.2% and 7.3% respectively.

WebAssembly (Wasm) is a low-level bytecode format that can run in modern browsers. With the development of standalone runtimes and the improvement of the WebAssembly System Interface (WASI), Wasm has further provided a more complete sandboxed runtime experience for server-side applications, effectively expanding its application scenarios. However, the implementation of WASI varies across different runtimes, and suboptimal interface implementations can lead to performance degradation during interactions between the runtime and the operating system. Existing research mainly focuses on overall performance evaluation of runtimes, while studies on WASI implementations are relatively scarce. To tackle this problem, we propose an eBPF-based WASI performance analysis framework. It collects key performance metrics of the runtime under different I/O load conditions, such as total execution time, startup time, WASI execution time, and syscall time. We can comprehensively analyze the performance of the runtime's I/O interactions with the operating system. Additionally, we provide a detailed analysis of the causes behind two specific WASI performance anomalies. These analytical results will guide the optimization of standalone runtimes and WASI implementations, enhancing their efficiency.