Predictive Reliability Engineering for Cloud Scale Business Intelligence Platforms through Anomaly Detection Capacity Optimization and Proactive Support Automation

Modern enterprises increasingly rely on large scale Business Intelligence (BI) platforms deployed across distributed cloud infrastructures to generate real time analytical insights for operational and strategic decision making. The the reliability of such platforms is frequently challenged by workload volatility, resource contention, data pipeline disruptions, and infrastructure anomalies that degrade dashboard availability and analytic accuracy. This study introduces a Predictive Reliability Engineering Framework for Cloud Scale Business Intelligence Platforms (PRE BI) that integrates anomaly detection, adaptive capacity optimization, and proactive support automation within a cloud native ecosystem. The framework leverages containerized microservices, Kubernetes orchestration, and machine learning driven anomaly detection models to monitor telemetry from data ingestion pipelines, compute clusters, and dashboard services. By analyzing historical operational metrics and system behavior patterns, the architecture predicts potential reliability degradation and dynamically allocates resources to prevent service disruption. The model incorporates automated incident response and self healing mechanisms capable of isolating failing components, rebalancing workloads, and triggering predictive support actions before end users experience performance issues. A reliability optimization metric Predictive Platform Reliability Index (PPRI) is proposed to quantify improvements across anomaly detection precision, resource elasticity, and service recovery time. Simulation experiments across multi domain enterprise scenarios demonstrate improved dashboard availability, reduced incident resolution time, and enhanced operational resilience. The proposed framework provides an architectural blueprint for building autonomous, self optimizing BI infrastructures capable of sustaining reliable analytics in complex cloud scale environments.