Semantic Observability for Autonomous Systems Diagnostics· 语义可观察性实现自主系统诊断
Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastructure. Isolating root causes during cascading failures remains an arduous task for Site Reliability Engineers (SREs), bottlenecked by manual keyword filtering and disjointed temporal correlation across monitoring dashboards. We introduce Semantic Observability, an autonomous multi-agent framework that unifies disparate log streams into a dynamic, queryable Property Graph Database. By modeling physical infrastructure topologies, logical software dependencies, and chronological event sequences as attributed nodes and directed relations, it bridges the semantic gap inherent in flat, unstructured log repositories. Building o
通过构建属性图数据库，实现自主系统诊断中的语义可观察性。
- 核心方法
- 提出Semantic Observability框架，将不同日志流统一为动态、可查询的属性图数据库，通过建模物理拓扑、逻辑依赖和时间事件序列来桥接语义差距。
- 适合谁读
- 适合研究者、工程师和SREs阅读
- 要解决的问题
- 现代分布式微服务部署中，异构、多格式的可观察性日志量巨大，导致系统故障时难以快速定位根因。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种新的自主多代理系统，能够有效地管理和分析大规模日志数据，帮助快速定位系统故障。
- 意义与局限
- 对提高分布式系统的可靠性和维护效率有重要影响，但仍需实际应用中的验证和优化。