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

TL;DR

CHASE is a novel framework that uses a causal hypergraph approach to efficiently identify root causes of anomalies in complex multimodal microservice systems, improving accuracy over existing methods.

Contribution

The paper introduces CHASE, a new hypergraph-based framework that models causality in multimodal data for root cause analysis in microservices.

Findings

CHASE outperforms state-of-the-art methods with up to 36.2% accuracy gain.

The framework effectively integrates traces, logs, and metrics for anomaly detection.

Experimental results validate CHASE's superior performance on real datasets.

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 in 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.