Automated Root Cause Analysis in Microservice Architectures: Leveraging Distributed Trace Correlation with OpenTelemetry for Faster Incident Resolution

Abstract

When something breaks in a microservice system, the hardest part is rarely fixing the bug it is finding it. As organizations decompose monolithic applications into hundreds of loosely coupled services, the blast radius of a single failure can ripple across service boundaries in ways that are notoriously difficult to trace by hand. Traditional monitoring approaches, built for simpler architecture, tend to generate a flood of alerts during an incident without pointing engineers toward the actual origin of the problem. This paper proposes a practical model for automated root cause analysis (RCA) that leverages distributed trace correlation through OpenTelemetry, the increasingly dominant open standard for observability instrumentation. The model combines trace topology reconstruction, latency anomaly detection, and error propagation scoring to narrow the search space during incidents and surface the most probable root cause with minimal human intervention. Drawing on data from three production microservice environments observed between late 2022 and early 2023, the paper demonstrates that trace-based automated RCA can reduce mean time to root cause identification by 60-78% compared to manual investigation, while significantly lowering the cognitive burden on on-call engineers during high-pressure incidents.