Self-Healing Pipeline Automation Using Observability-Driven Feedback Mechanisms in Cloud-Native Distributed Systems

Abstract

Recent cloud-native distributed systems are more and more relying on automated data pipelines to make real-time decisions and analytics. Nevertheless, such pipelines are very susceptible to failures due to contention of the resources, unstable network, and changing workloads that result in poor performance and system unavailability. The current pipeline management methods are mainly reactive and can only offer manual intervention or fixed rule-based recovery, which cannot be effective in dynamic environments. In this paper, a self-healing pipeline automation framework based on observability is suggested and consists of continuous monitoring, anomaly detection, root cause analysis, and automated recovery in the framework of a closed loop feedback mechanism. The proposed system uses real-time observability metrics with the help of latency, throughput, failure rate, and resource utilization to calculate a dynamical health score and detect anomalous conditions. Decision engine is developed to choose the best recovery steps, including rescheduling of tasks, scaling of resources and workload rerouting, depending on the present system state. The system is deployed in a cloud-native experience in a container orchestration and distributed processing platform. The experimental assessment proves that the proposed solution decreases recovery time by 45 percent, increases throughput stability, and fault resilience in contrast with traditional rule-based systems. The findings demonstrate the usefulness of the feedback-based automation towards enhancing the reliability and scalability of the pipeline systems distributed.