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

TL;DR

AnoMod is a comprehensive, multimodal dataset for anomaly detection and root cause analysis in microservice systems, enabling more realistic evaluation and development of troubleshooting methods.

Contribution

We introduce AnoMod, a new multimodal dataset with diverse anomalies and system modalities, addressing the lack of high-quality datasets for MSS anomaly research.

Findings

Provides five modalities per scenario for richer analysis

Includes four categories of realistic anomalies

Enables evaluation of cross-modal detection and RCA methods

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.