# MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems

arXiv: https://arxiv.org/abs/2103.01782 · HTML: https://mlsanity.com/paper/2103.01782/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 30 of 36)

- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [Root Cause Analysis In Microservice Using Neural Granger Causal Discovery](https://mlsanity.com/paper/2402.01140/) (2024)
- [Root Cause Analysis of Outliers with Missing Structural Knowledge](https://mlsanity.com/paper/2406.05014/) (2024)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [The PetShop Dataset -- Finding Causes of Performance Issues across Microservices](https://mlsanity.com/paper/2311.04806/) (2023)
- [TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems](https://mlsanity.com/paper/2504.20462/) (2025)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps](https://mlsanity.com/paper/2204.11598/) (2022)
- [Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System](https://mlsanity.com/paper/2310.04701/) (2023)
- [Robust Root Cause Diagnosis using In-Distribution Interventions](https://mlsanity.com/paper/2505.00930/) (2025)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)
- [ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios](https://mlsanity.com/paper/2503.01475/) (2025)
- [GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks](https://mlsanity.com/paper/2504.00058/) (2025)
- [Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments](https://mlsanity.com/paper/2406.13604/) (2024)
- [CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems](https://mlsanity.com/paper/2406.19711/) (2024)
- [A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management](https://mlsanity.com/paper/2407.14532/) (2024)
- [Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems](https://mlsanity.com/paper/2305.03331/) (2023)
- [AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study](https://mlsanity.com/paper/2305.16092/) (2023)
- [KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks](https://mlsanity.com/paper/2402.13264/) (2024)
- [Online Multi-modal Root Cause Identification in Microservice Systems](https://mlsanity.com/paper/2410.10021/) (2024)
- [STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices](https://mlsanity.com/paper/2605.15581/) (2026)
- [UModel: An Agent-Ready Observability Data Modeling Method at Scale](https://mlsanity.com/paper/2606.04799/) (2026)
- [LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding](https://mlsanity.com/paper/2205.08362/) (2022)
- [Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis](https://mlsanity.com/paper/2305.10638/) (2023)
- [Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation](https://mlsanity.com/paper/2508.01635/) (2025)
- [A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)](https://mlsanity.com/paper/2510.11141/) (2025)
- [Score-based Integrated Gradient for Root Cause Explanations of Outliers](https://mlsanity.com/paper/2601.22399/) (2026)
- [Causal explanations of outliers in systems with lagged time-dependencies](https://mlsanity.com/paper/2602.04667/) (2026)
- [PRIM: Meta-Learned Bayesian Root Cause Analysis](https://mlsanity.com/paper/2605.08786/) (2026)

## Abstract (arXiv)

Availability issues of industrial microservice systems (e.g., drop of successfully placed orders and processed transactions) directly affect the running of the business. These issues are usually caused by various types of service anomalies which propagate along service dependencies. Accurate and high-efficient root cause localization is thus a critical challenge for large-scale industrial microservice systems. Existing approaches use service dependency graph based analysis techniques to automatically locate root causes. However, these approaches are limited due to their inaccurate detection of service anomalies and inefficient traversing of service dependency graph. In this paper, we propose a high-efficient root cause localization approach for availability issues of microservice systems, called MicroHECL. Based on a dynamically constructed service call graph, MicroHECL analyzes possible anomaly propagation chains, and ranks candidate root causes based on correlation analysis. We combine machine learning and statistical methods and design customized models for the detection of different types of service anomalies (i.e., performance, reliability, traffic). To improve the efficiency, we adopt a pruning strategy to eliminate irrelevant service calls in anomaly propagation chain analysis. Experimental studies show that MicroHECL significantly outperforms two state-of-the-art baseline approaches in terms of both accuracy and efficiency. MicroHECL has been used in Alibaba and achieves a top-3 hit ratio of 68% with root cause localization time reduced from 30 minutes to 5 minutes.