SH-PDOPS: A Self-Healing Predictive DevOps Framework for Autonomous Cloud-Native Deployments

Modern DevOps systems primarily focus on deployment automation but often lack autonomous recovery and predictive operational intelligence. This paper proposes SH-PDOPS (Self-Healing Predictive DevOps System), a cloud-native DevOps framework designed to enhance deployment reliability through predictive failure analysis, automated remediation, and observability-driven monitoring. The proposed system integrates CI/CD pipelines, Docker-based containerization, cloud deployment workflows, AI-assisted anomaly detection, and automated rollback mechanisms to minimize deployment failures and operational downtime. It continuously monitors application behavior, predicts potential risks using historical deployment patterns and runtime signals, and executes autonomous recovery actions such as rollback, restart, or redeployment when anomalies are detected. Experimental evaluation shows that SH-PDOPS improves deployment stability, reduces Mean Time to Recovery (MTTR), lowers manual intervention rates, and increases overall system reliability compared to traditional CI/CD pipelines.