A Cascaded Graph Neural Network for Joint Root Cause Localization and Analysis in Edge Computing Environments

TL;DR

This paper introduces a cascaded graph neural network framework that improves scalability and reduces inference latency for root cause localization and analysis in large, distributed edge computing environments, while maintaining high diagnostic accuracy.

Contribution

It proposes a novel cascaded GNN architecture with communication-driven clustering to enable scalable, low-latency root cause analysis in complex edge computing microservice systems.

Findings

Achieves comparable accuracy to centralized GNNs

Maintains near-constant inference latency with increasing graph size

Demonstrates effectiveness on MicroCERCL benchmark and large-scale datasets

Abstract

Edge computing environments host increasingly complex microservice-based IoT applications that are prone to performance anomalies propagating across dependent services. Identifying the faulty component (root cause localization) and the underlying fault type (root cause analysis) is essential for timely mitigation. Supervised graph neural networks (GNNs) currently represent the state of the art for joint root cause localization and analysis. However, existing approaches rely on centralized processing over full-system graphs, leading to high inference latency and limited scalability in large, distributed edge environments. In this paper, we propose a cascaded GNN framework for joint RCL and fault type identification that explicitly addresses these scalability challenges. Our approach employs communication-driven clustering to partition large service graphs into highly interacting communities and a cascaded network with two subnetworks that perform hierarchical RCL/RCA. By restricting message passing to reduced and structured subgraphs, the proposed framework significantly lowers computational complexity while preserving critical dependency information. We evaluate the proposed method on the MicroCERCL benchmark and large-scale datasets generated using the iAnomaly simulation framework. Experimental results show that the cascaded architecture achieves diagnostic accuracy comparable to centralized GNN baselines while maintaining near-constant inference latency as graph size increases, enabling scalable and actionable AIOps in edge computing environments.