AI-Driven Cloud Infrastructure Optimization for Dynamic Workload Management

Abstract

Heterogeneous and dynamic workloads provide continuing challenges with an efficient management of cloud infrastructure and frequently results in underutilization of resources, deterioration of performance and high operational costs. This paper presents a cloud infrastructure optimization system architecture based on AI-driven self-controlling of dynamic workload changes by means of smart resources reallocation and dynamic scaling. The framework uses machine learning to help improve the optimization of compute, storage, and network resources in real time by learning workload patterns and behavior of the infrastructure through the application of reinforcement learning. The suggested solution contributes to the responsiveness of the system, better utilization of resources, and fewer service-level agreement (SLA) breaches as opposed to traditional rule-based and reactive auto-scaling. The practical test proves the usefulness of AI-oriented optimization in ensuring cost efficiency, scalability, and operational resiliency in instances of clouds. The results show the promise of AI-based solutions to allow self-optimizing and autonomous cloud architectures that can be used to support workload-intensive applications in the present day.