AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems

2026-01-30

AI Summary

This work addresses the scarcity of high-quality, publicly available multimodal datasets in microservice systems, which has hindered research on anomaly detection and root cause analysis. To bridge this gap, the authors introduce AnoMod, a novel dataset constructed by injecting four types of anomalies—spanning performance, service, database, and code layers—into two open-source microservice benchmarks, SocialNetwork and TrainTicket. Concurrently, five modalities of system telemetry are collected: logs, metrics, distributed traces, API responses, and code coverage, enabling an end-to-end fault simulation environment. AnoMod supports the evaluation of cross-modal anomaly detection approaches and facilitates fine-grained root cause localization at both service and code levels, thereby advancing end-to-end fault diagnosis in microservice architectures.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Microservice systems (MSS) have become a predominant architectural style for cloud services. Yet the community still lacks high-quality, publicly available datasets for anomaly detection (AD) and root cause analysis (RCA) in MSS. Most benchmarks emphasize performance-related faults and provide only one or two monitoring modalities, limiting research on broader failure modes and cross-modal methods. To address these gaps, we introduce a new multimodal anomaly dataset built on two open-source microservice systems: SocialNetwork and TrainTicket. We design and inject four categories of anomalies (Ano): performance-level, service-level, database-level, and code-level, to emulate realistic anomaly modes. For each scenario, we collect five modalities (Mod): logs, metrics, distributed traces, API responses, and code coverage reports, offering a richer, end-to-end view of system state and inter-service interactions. We name our dataset, reflecting its unique properties, as AnoMod. This dataset enables (1) evaluation of cross-modal anomaly detection and fusion/ablation strategies, and (2) fine-grained RCA studies across service and code regions, supporting end-to-end troubleshooting pipelines that jointly consider detection and localization.

Problem

• anomaly detection
• root cause analysis
• microservice systems
• multimodal dataset
• failure modes

Innovation

• multimodal dataset
• anomaly detection
• root cause analysis
• microservice systems
• cross-modal fusion