Adaptive AI Model Selection for Cloud Infrastructure Optimization: A Framework for Intelligent, Self-Regulating Computing Environments

Abstract

Contemporary cloud computing ecosystems demand intelligent infrastructure management strategies that transcend traditional static provisioning models. The adaptive AI model selection framework presented herein addresses fundamental challenges in managing heterogeneous workloads across microservices architectures, data processing pipelines, IoT data streams, and AI inference engines through systematic integration of machine learning operations with event-driven automation mechanisms. The framework synthesizes cloud engineering principles, Infrastructure as Code methodologies, and intelligent model selection algorithms to enable real-time optimization based on telemetry analysis, historical workload patterns, and operational objectives. Through automated training pipelines, continuous evaluation protocols, and progressive deployment strategies with rollback capabilities, the framework facilitates a self-optimizing infrastructure that minimizes human intervention while maintaining service-level agreements and cost efficiency. The architecture comprises telemetry collection subsystems, versioned model repositories, intelligent selection engines implementing multi-criteria decision frameworks, and automated deployment orchestration utilizing canary patterns and circuit breakers. Event-driven automation enables real-time responsiveness through stream processing frameworks that evaluate optimization opportunities via windowed computations, complex event processing patterns, and stateful processing mechanisms. Enhancements to multi-cloud and hybrid environments support heterogeneous resource abstractions, cross-platform data movement limits, and vendor-specific operational behaviors by using cloud-agnostic abstraction layers and provider-specific adapters. The framework illustrates how smart and dynamic infrastructure operation can make organizations realize better resource utilization, better service reliability, and reduction of operational expenses in da distributed computing environment, as well as provision of regulatory compliance and data sovereignty requirements in a complex multi-cloud deployment.