Resilient Cloud-Native Platforms for Real-Time AI-Enabled Enterprise Application Performance Management
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Abstract
The increasing dependence of enterprises on cloud-native applications has created a critical need for intelligent, resilient, and real-time application performance management. Modern enterprise platforms operate across containers, Kubernetes clusters, microservices, APIs, serverless functions, databases, and multi-cloud environments, generating highly dynamic operational conditions that traditional monitoring approaches often struggle to manage. This paper proposes a resilient cloud-native platform architecture that integrates artificial intelligence, real-time observability, predictive analytics, automated anomaly detection, and self-healing mechanisms for enterprise application performance management. The proposed framework continuously collects telemetry from applications, infrastructure, networks, containers, and cloud services and applies AI-driven analytics to identify performance degradation, predict resource bottlenecks, detect abnormal behavior, and recommend or execute corrective actions. A unified observability layer combines metrics, logs, traces, events, and business-level indicators to establish comprehensive application visibility. Machine-learning models analyze historical and streaming data to forecast latency, resource saturation, failures, and service-level objective violations. An intelligent orchestration layer translates AI predictions into automated scaling, workload redistribution, configuration optimization, service recovery, and incident-prioritization actions. Resilience is strengthened through redundancy, fault isolation, adaptive resource allocation, graceful degradation, and continuous health evaluation. The proposed architecture aims to improve application availability, reduce mean time to detection and recovery, optimize cloud resource utilization, and maintain consistent performance under workload fluctuations and infrastructure failures
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