Federated AI-Driven Resource Optimization in Multi-Cloud Environments

Abstract

Enterprises are increasingly adopting multi-cloud infrastructures to increase service availability, scalability, fault tolerance and vendor independence. Nevertheless, the allocation of resources among heterogeneous cloud providers is a highly critical issue because the workloads are variable, the latency is limited, the SLA compliance issues are present, and the privacy concerns are also central to the centralized scheduling models. Conventional centralized reinforcement schedulers in learning demand complete visibility of workload, which can cause overheads in the communication and expose risks of data disclosure. In this paper, we suggest a Federated Reinforcement Learning Scheduler (FRLS) in privacy-sensitive and adaptive optimization of resources on multi-clouds. The proposed architecture has every cloud node separately train a reinforcement learning-based scheduling agent on local observations of workload. Rather than exchanging raw data, nodes exchange model parameters with a federated aggregation server on an irregular basis and weighted averaging is used to create a global scheduling model. Simulations with synthetic heterogeneous workload traces show that FRLS utilizes its resources 15-18 percent more efficiently, SLA violations 8-10 percent fewer and converge quicker than heuristic and centralized RL schedulers. The framework offers scalable, secure and distributed intelligence on next-generation multi-cloud orchestration systems.