AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems
Ke Ping, Hamza Bin Mazhar, Yuqing Wang, Ying Song, Mika V. M\"antyl\"a

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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · Software-Defined Networks and 5G
