Abstract

Cloud infrastructure underpins modern healthcare systems, financial platforms, artificial-intelligence services, and public-sector applications. In these environments, infrastructure managers must continuously balance service-level objectives against rising compute cost. Reactive autoscaling remains the dominant operational mechanism in practice, but threshold-based policies expand capacity only after congestion has already appeared, which can produce latency spikes, brief SLA violations, and persistent over-provisioning. This paper presents a practical AI-driven predictive resource management framework for scalable and resilient cloud infrastructure. The framework combines workload forecasting, multivariate anomaly detection, policy-aware decision logic, and cloud-native orchestration to allocate resources before demand peaks occur. A prototype evaluation using representative cyclical and bursty workloads compares static provisioning, reactive autoscaling, and predictive scaling. The predictive approach reduces total compute-hours by 22.2% versus reactive autoscaling and 36.4% versus static provisioning, while improving SLA compliance to 99.1% and increasing average utilization to 76%. The paper also discusses design trade-offs, deployment constraints, and portability across Kubernetes-based environments. The results suggest that predictive resource management can materially improve both resilience and cost efficiency when integrated with disciplined observability and automated control loops.

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