AI-Driven Resource Optimization in Multi-Cloud Environments

Abstract

This research explores advanced strategies for optimizing resource allocation across multi-cloud environments by harnessing artificial intelligence (AI). As organizations increasingly adopt multi-cloud architectures to enhance flexibility and resilience, managing distributed resources efficiently becomes highly complex. The study presents an AI-driven framework that intelligently predicts workload demands, identifies optimal resource distribution, and dynamically orchestrates computing, storage, and networking assets across diverse cloud platforms. The framework leverages machine learning algorithms to analyze real-time and historical performance metrics, enabling automated scaling and cost-effective provisioning in response to fluctuating workloads. Additionally, it incorporates predictive analytics to mitigate risks associated with resource contention and service outages, thereby maintaining service quality and operational continuity. Through simulation and empirical evaluation, the proposed approach demonstrates significant improvements in utilization, operational cost, and service reliability compared to conventional static and rule-based resource management techniques. The findings highlight the transformative potential of AI technologies in addressing the unique challenges of multi-cloud environments and provide actionable insights for enterprises aiming to optimize their cloud operations while minimizing complexity and expenses.