ML-Based Resource Allocation In Hybrid Cloud Systems

Machine Learning (ML)-based resource allocation in hybrid cloud systems represents a pivotal shift from static, rule-based management to dynamic, intelligent orchestration. As enterprises increasingly adopt hybrid architectures—combining private infrastructure with public cloud scalability—the complexity of managing heterogeneous resources grows exponentially. This review explores the integration of advanced ML algorithms, including Reinforcement Learning (RL), Deep Learning (DL), and Meta-heuristics, to optimize computational efficiency, minimize operational costs, and ensure Quality of Service (QoS). Traditional heuristic approaches often fail to account for the volatile nature of cloud workloads and the latency variations inherent in hybrid environments. By leveraging predictive analytics, ML models can anticipate demand spikes and proactively scale resources, effectively balancing the load between local servers and public providers. This article synthesizes current methodologies, highlighting the transition toward autonomous \\\"self-healing\\\" systems. The ultimate goal of ML-driven allocation is to achieve a seamless, cost-effective, and energy-efficient infrastructure that adapts to real-time industrial requirements without manual intervention.