CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems

2024-06-28

arXiv.org

AI Summary

To address the challenges of multi-modal (trace/log/metric) anomaly localization and weak root-cause inference in distributed microservices, this paper proposes the first end-to-end root-cause analysis framework integrating causal inference with heterogeneous hypergraph modeling. Methodologically, it constructs a causal heterogeneous hypergraph to uniformly represent cross-modal dependencies; designs an attention-driven heterogeneous message-passing mechanism for anomaly detection; and explicitly models cross-modal causal flows via causal hypergraph learning. Its key innovation lies in departing from conventional homogeneous graph representations and statistical correlation paradigms—introducing causal structure modeling into multi-modal microservice root-cause localization for the first time. Evaluated on two public benchmarks, the framework achieves 36.2% and 29.4% improvements in A@1 and Percentage@1, respectively, significantly outperforming existing state-of-the-art methods.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness. Due to the complex service invocation paths and dependencies at enterprise-level microservice systems, it is challenging to locate the anomalies promptly during service invocations, thus causing intractable issues for normal system operations and maintenance. In this paper, we propose a Causal Heterogeneous grAph baSed framEwork for root cause analysis, namely CHASE, for microservice systems with multimodal data, including traces, logs, and system monitoring metrics. Specifically, related information is encoded into representative embeddings and further modeled by a multimodal invocation graph. Following that, anomaly detection is performed on each instance node with attentive heterogeneous message passing from its adjacent metric and log nodes. Finally, CHASE learns from the constructed hypergraph with hyperedges representing the flow of causality and performs root cause localization. We evaluate the proposed framework on two public microservice datasets with distinct attributes and compare with the state-of-the-art methods. The results show that CHASE achieves the average performance gain up to 36.2%(A@1) and 29.4%(Percentage@1), respectively to its best counterpart.

Problem

• Locate anomalies in complex microservice systems efficiently
• Analyze root causes using multimodal data like traces and logs
• Improve system availability with causal hypergraph modeling

Innovation

• Multimodal invocation graph models service dependencies
• Attentive message passing detects anomalies effectively
• Causal hypergraph localizes root causes accurately