STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management
cs.LG, cs.AI
Submitted: 2026-09-12
Updated: 2026-09-12
Code: https://github.com/XinhuaMiao/STAR
License: http://creativecommons.org/licenses/by/4.0/
The gist: Automated incident management in large-scale microservice systems relies on learning robust representations from multimodal observability data, including metrics, logs, and traces.
Terminology
Abstract
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.
Sources
- Layer Normalization
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Temporal Graph Networks for Deep Learning on Dynamic Graphs
- TrioXpert: An Automated Incident Management Framework for Microservice System
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