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Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis

Gijun Park

TL;DR

This work addresses the modality gap between time-series telemetry and pretrained language model embeddings in cloud incident RCA by introducing TimeRAG, a multimodal framework. TimeRAG combines (i) Patch Segmentation with single-token representations, (ii) a Time Series Encoder that maps temporal patches into a restricted LLM vocabulary via gated cross-attention, and (iii) a Retrieval-Augmented Generation pipeline that consults a vector store of historical incidents to produce expert-level diagnoses. The training objective blends alignment and classification losses, $L = L_{\text{align}} + L_{\text{clf}}$, to align time-series embeddings with the LLM space while predicting failure types, aided by a reflection loop that can iterate up to 5 times. Evaluated on six cloud RCA benchmarks, TimeRAG achieves up to $48.75\%$ diagnostic accuracy, demonstrating strong performance on complex, multi-failure scenarios and highlighting the practical potential for automated RCA and reduced MTTR in production SRE workflows.

Abstract

Root cause analysis in modern cloud infrastructure demands sophisticated understanding of heterogeneous data sources, particularly time-series performance metrics that involve core failure signatures. While large language models demonstrate remarkable capabilities in textual reasoning, their discrete token-based architecture creates fundamental incompatibilities with continuous numerical sequences exhibiting temporal dependencies. Current methodologies inadequately address this modality mismatch, constraining the potential of language model-driven automation in incident management workflows. This paper presents a multimodal diagnostic framework that harmonizes time-series representations with pretrained language model embedding spaces. Our approach contributes three technical advances: (1) a semantic compression technique that distills temporal segments into single-token abstractions while preserving pattern semantics, (2) an alignment encoder utilizing gated cross-attention to project time-series features into language model latent space, and (3) a retrieval-augmented diagnostic pipeline that synthesizes aligned embeddings with historical incident knowledge for expert-level failure attribution. Comprehensive evaluation across six cloud system benchmarks demonstrates that our framework achieves leading performance, reaching 48.75% diagnostic accuracy with notable improvements on scenarios involving compound failure modes. The results validate embedding-space alignment as an effective strategy for enabling language models to reason over multimodal telemetry data in production incident response contexts.

Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis

TL;DR

This work addresses the modality gap between time-series telemetry and pretrained language model embeddings in cloud incident RCA by introducing TimeRAG, a multimodal framework. TimeRAG combines (i) Patch Segmentation with single-token representations, (ii) a Time Series Encoder that maps temporal patches into a restricted LLM vocabulary via gated cross-attention, and (iii) a Retrieval-Augmented Generation pipeline that consults a vector store of historical incidents to produce expert-level diagnoses. The training objective blends alignment and classification losses, , to align time-series embeddings with the LLM space while predicting failure types, aided by a reflection loop that can iterate up to 5 times. Evaluated on six cloud RCA benchmarks, TimeRAG achieves up to diagnostic accuracy, demonstrating strong performance on complex, multi-failure scenarios and highlighting the practical potential for automated RCA and reduced MTTR in production SRE workflows.

Abstract

Root cause analysis in modern cloud infrastructure demands sophisticated understanding of heterogeneous data sources, particularly time-series performance metrics that involve core failure signatures. While large language models demonstrate remarkable capabilities in textual reasoning, their discrete token-based architecture creates fundamental incompatibilities with continuous numerical sequences exhibiting temporal dependencies. Current methodologies inadequately address this modality mismatch, constraining the potential of language model-driven automation in incident management workflows. This paper presents a multimodal diagnostic framework that harmonizes time-series representations with pretrained language model embedding spaces. Our approach contributes three technical advances: (1) a semantic compression technique that distills temporal segments into single-token abstractions while preserving pattern semantics, (2) an alignment encoder utilizing gated cross-attention to project time-series features into language model latent space, and (3) a retrieval-augmented diagnostic pipeline that synthesizes aligned embeddings with historical incident knowledge for expert-level failure attribution. Comprehensive evaluation across six cloud system benchmarks demonstrates that our framework achieves leading performance, reaching 48.75% diagnostic accuracy with notable improvements on scenarios involving compound failure modes. The results validate embedding-space alignment as an effective strategy for enabling language models to reason over multimodal telemetry data in production incident response contexts.
Paper Structure (15 sections, 4 equations, 1 figure, 3 tables, 1 algorithm)

This paper contains 15 sections, 4 equations, 1 figure, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Patch Segmentation and Single-token Representation
  5. Time Series Encoder
  6. Integreted RAG Agent
  7. Experiments
  8. Experimental Setup
  9. Datasets
  10. Preprocessing
  11. Baselines
  12. Evaluation Metric
  13. Results
  14. Conclusion
  15. Limitations and Future Work

Figures (1)

  • Figure 1: TimeRAG overview. (a) time-series performance metrics are segmented into fixed-length patches, with each patch compressed into a single-token representation; (b) the Time Series Encoder maps the patch tokens into the pretrained LLM embedding space via gated cross-attention; and (c) the RAG diagnostic agent generates root-cause diagnoses based on aligned performance metrics and retrieved nearest incident embeddings from a vector store, with LLMs.