Reliability Modeling and Automated Incident Response for Cadence Based Distributed Systems SRE Signals and RCA Automation

Abstract Large distributed systems often rely on workflow engines such as Cadence to manage long running tasks across many services. When failures occur, traditional incident response systems mainly focus on fixing problems quickly, without measuring how recovery decisions affect long term system stability. This study proposes a reliability aware automated incident response framework that combines workflow level reliability modeling, monitoring signals, and automated root cause identification. Workflow execution is modeled as a set of states that can fail or recover, allowing the system to estimate overall availability. Monitoring data is enriched with workflow information to detect problems faster and identify their true causes more accurately. Experimental evaluation on a distributed testbed shows that the proposed approach reduces detection and recovery time while improving steady state availability and lowering repeated failures. The results demonstrate that integrating reliability modeling directly into automated incident handling not only speeds up response but also strengthens long term system stability. This framework provides a structured way to build more dependable distributed systems.