Resilient Enterprise Infrastructure through Autonomous Machine Learning and Intelligent Cloud-Native Monitoring

Abstract

Enterprise infrastructure has become increasingly complex as organizations adopt cloud-native architectures, distributed applications, microservices, containers, serverless computing, and hybrid or multi-cloud environments. This complexity creates significant challenges for maintaining availability, performance, security, and operational continuity. Traditional infrastructure monitoring approaches, which largely depend on predefined thresholds and manual intervention, are often insufficient for identifying emerging failures and responding to rapidly changing workloads. This study examines the role of autonomous machine learning and intelligent cloud-native monitoring in developing resilient enterprise infrastructure. Autonomous machine learning can continuously analyze operational telemetry, identify anomalies, predict infrastructure failures, correlate events, and support automated remediation. Cloud-native monitoring complements these capabilities by collecting and interpreting metrics, logs, traces, events, and application-level signals across distributed environments. The proposed research methodology integrates machine-learning-based anomaly detection, predictive analytics, observability practices, and automated response mechanisms within a controlled enterprise infrastructure environment. The study evaluates resilience through indicators such as availability, mean time to detect, mean time to recover, prediction accuracy, false-positive rate, resource utilization, and service performance. The research aims to demonstrate how intelligent monitoring can transform infrastructure operations from reactive fault management toward proactive and autonomous resilience