AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914295228399616 |
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| author | Ping, Ke Mazhar, Hamza Bin Wang, Yuqing Song, Ying Mäntylä, Mika V. |
| author_facet | Ping, Ke Mazhar, Hamza Bin Wang, Yuqing Song, Ying Mäntylä, Mika V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems Ping, Ke Mazhar, Hamza Bin Wang, Yuqing Song, Ying Mäntylä, Mika V. Software Engineering 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. |
| title | AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2601.22881 |