The Impact Of Machine Learning On Dynamic Resource Allocation In Multi-Cloud Architectures
Résumé
The integration of machine learning (ML) into multi-cloud architectures has revolutionized the way organizations manage and allocate resources dynamically. Traditional static allocation models often fail to address the variability and unpredictability of workloads across heterogeneous cloud environments. ML-driven systems enable proactive, data-driven decisions that optimize cost, performance, and reliability. By leveraging predictive analytics, reinforcement learning, and adaptive algorithms, resource utilization can be adjusted in real time to meet service-level agreements (SLAs) efficiently. Moreover, ML enhances automation, reduces human intervention, and mitigates latency or overprovisioning issues. This review explores the methodologies, frameworks, and benefits of ML-based resource allocation within multi-cloud infrastructures, highlighting the evolving role of artificial intelligence in managing distributed computing environments. It also discusses major challenges, including data privacy, model interpretability, and cross-cloud interoperability, while outlining future research directions aimed at building intelligent, self-optimizing multi-cloud systems.