arXiv:2606.09942v1 Announce Type: cross Abstract: Microservice systems are widely used to build cloud applications, yet their complexity makes failures inevitable, degrading user experience and causing economic loss. Automated anomaly detection and root cause analysis (RCA) are now active research areas, but existing techniques share five limitations. First, most treat anomaly detection and RCA separately, assuming anomalies are detected correctly, and falter when detection is imprecise due to noise or delay. Second, they focus on metrics, logs, and traces, leaving event data such as API calls
The increasing complexity and widespread adoption of microservice architectures in cloud applications necessitate more sophisticated anomaly detection and root cause analysis due to inevitable failures.
This research addresses a critical limitation in current AI/ML operations by integrating anomaly detection and root cause analysis, which is vital for maintaining the reliability and performance of cloud infrastructure.
This research proposes a more robust approach to system reliability by overcoming the limitations of current anomaly detection and RCA techniques, especially concerning precision and diverse data types beyond metrics.
- · Cloud infrastructure providers
- · DevOps teams
- · AI/ML operations platforms
- · SaaS companies
- · Companies with high MTTR (Mean Time To Recovery)
- · Traditional monitoring solutions
- · Manual incident response teams
Improved system reliability and reduced downtime for cloud-native applications.
Increased trust and adoption of complex microservice architectures for business-critical functions.
Enhanced overall productivity and reduced operational costs for organizations reliant on cloud-based systems.
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Read at arXiv cs.AI