STAR：面向统一微服务事件管理的自适应时空归一化
STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management
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中文总结 AI 辅助
本文提出STAR框架，通过时间与空间自适应归一化，统一处理微服务异常检测、故障分诊和根因定位，在两个真实基准上超越现有方法。
中文摘要 AI 辅助
大规模微服务系统中的自动化事件管理依赖于从多模态可观测性数据（包括指标、日志和链路追踪）中学习鲁棒的表示。尽管最近的自监督框架能够对异常检测（AD）、故障分诊（FT）和根因定位（RCL）进行统一建模，但它们往往难以应对非平稳的时间动态和异构的服务依赖结构。在本文中，我们提出了STAR，一个时空自适应表示学习框架，通过自适应归一化明确解决这些挑战。STAR引入了两个紧密耦合的机制：时间自适应归一化（TAN），利用多尺度时间上下文动态归一化多元时间序列；以及空间自适应归一化（SAN），在服务依赖图上执行结构感知的归一化。与先前将归一化视为静态或任务无关的方法不同，STAR将其表述为一种可学习的、上下文条件化的变换，与微服务系统的内在属性对齐。由此产生的自适应表示被集成到一个统一的自监督框架中，为AD、FT和RCL任务提供端到端的无监督支持。在两个真实世界微服务基准上的大量实验表明，STAR在所有最先进的基线上持续取得更优性能，在所有三个任务上产生显著且稳定的改进。我们的结果强调了自适应归一化作为复杂软件系统中鲁棒多模态表示学习的一种有原则且有效的机制。
英文摘要
Automated incident management in large-scale microservice systems relies on learning robust representations from multimodal observability data, including metrics, logs, and traces. Although recent self-supervised frameworks enable unified modeling for anomaly detection (AD), failure triage (FT), and root cause localization (RCL), they often struggle with non-stationary temporal dynamics and heterogeneous service dependency structures. In this paper, we propose STAR, a Spatial-Temporal Adaptive Representation learning framework that explicitly addresses these challenges through adaptive normalizations. STAR introduces two tightly coupled mechanisms: Temporal Adaptive Normalization (TAN), which dynamically normalizes multivariate time series using multi-scale temporal context, and Spatial Adaptive Normalization (SAN), which performs structure-aware normalization over service dependency graphs. Unlike prior methods that treat normalization as static or task-agnostic, STAR formulates it as a learnable, context-conditioned transformation aligned with the intrinsic properties of microservice systems. The resulting adaptive representations are integrated into a unified self-supervised framework, enabling end-to-end unsupervised support for AD, FT, and RCL tasks. Extensive experiments on two real-world microservice benchmarks demonstrate that STAR consistently outperforms all state-of-the-art baselines, yielding significant and stable improvements across all three tasks. Our results highlight adaptive normalization as a principled and effective mechanism for robust multimodal representation learning in complex software systems.
