Intelligent Resource Allocation in Multi-Cloud Environments: An AI-Driven Approach

Abstract

The increasing adoption of multi-cloud computing—where enterprises distribute workloads across multiple cloud providers—has introduced new challenges in resource allocation, cost optimization, and performance management. Traditional static allocation approaches often fail to adapt dynamically to workload fluctuations, leading to resource inefficiencies, increased costs, and potential service disruptions. This article presents an AI-driven intelligent resource allocation framework that leverages machine learning, reinforcement learning, and metaheuristic optimization to efficiently distribute workloads across diverse cloud platforms. It incorporates predictive analytics to forecast resource demand and intelligent workload scheduling to balance computational loads while considering cost-performance trade-offs. Additionally, the article integrates software-defined networking to optimize cloud-to-cloud data transfer, ensuring low-latency execution for real-time applications. By integrating adaptive resource management, cost-aware scheduling, and real-time system monitoring, it contributes to more resilient, scalable, and cost-efficient multi-cloud ecosystems. The article provides valuable insights for enterprises, cloud service providers, and researchers seeking to optimize multi-cloud resource allocation through intelligent automation and AI-driven decision-making.