Federated deep learning-driven decentralized and cost-aware cloud resource management for load balancing and SLA optimizations

Abstract

Scalable, privacy-preserving, cost-effective, and dynamic workload-tolerant cloud resource management is needed. The federated and decentralized framework uses deep learning, reinforcement learning, edge–fog collaboration, transformer-based forecasting, and anomaly-aware post-processing. Together, these components provide proactive load balancing, adaptive provisioning, and increased service reliability across distributed cloud ecosystems. The architecture allows collaborative scheduling without regional workload data, cost-aware pre-emptive resource decisions, edge–fog nodes for latency-critical workloads, and transformer-based attention modeling for precise workload forecasts. Multi-regional deployment improves forecast accuracy, resource usage, latency, and SLA compliance in large-scale Google and Alibaba cloud traces. System SLA adherence is above 98%, accuracy above 95%, resource utilization above 25%, and latency below 30%. Comparative assessments demonstrate anomaly response and operational cost savings gains. The findings stress federated learning, decentralized scheduling, and cost-aware optimization. The architecture enables varied infrastructures, variable demand, and changing market conditions, enhancing system resilience. The research proposes leveraging the model to build next-generation cloud management systems with high adaptability, privacy assurances, and cheap overheads. Structured model design, operational process, and assessment methodologies enable reproducibility and expansions.