Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments

TL;DR

This paper introduces MicroCERCL, a novel method for accurately localizing root causes of failures in microservice systems within cloud-edge collaborative environments, addressing challenges like network instability and high latency.

Contribution

MicroCERCL is the first approach to localize root causes at both kernel and application levels in complex cloud-edge environments, utilizing log analysis and graph neural networks without relying on historical data.

Findings

Achieves at least 24.1% higher top-1 accuracy than existing methods.

Effectively localizes root causes in hybrid, unstable cloud-edge microservice deployments.

Introduces the first benchmark dataset for hybrid deployment microservice systems.

Abstract

With the development of cloud-native technologies, microservice-based software systems face challenges in accurately localizing root causes when failures occur. Additionally, the cloud-edge collaborative environment introduces more difficulties, such as unstable networks and high latency across network segments. Accurately identifying the root cause of microservices in a cloud-edge collaborative environment has thus become an urgent problem. In this paper, we propose MicroCERCL, a novel approach that pinpoints root causes at the kernel and application level in the cloud-edge collaborative environment. Our key insight is that failures propagate through direct invocations and indirect resource-competition dependencies in a cloud-edge collaborative environment characterized by instability and high latency. This will become more complex in the hybrid deployment that simultaneously involves multiple microservice systems. Leveraging this insight, we extract valid contents from kernel-level logs to prioritize localizing the kernel-level root cause. Moreover, we construct a heterogeneous dynamic topology stack and train a graph neural network model to accurately localize the application-level root cause without relying on historical data. Notably, we released the first benchmark hybrid deployment microservice system in a cloud-edge collaborative environment (the largest and most complex within our knowledge). Experiments conducted on the dataset collected from the benchmark show that MicroCERCL can accurately localize the root cause of microservice systems in such environments, significantly outperforming state-of-the-art approaches with an increase of at least 24.1% in top-1 accuracy.