Federated Control for Stable Resource Coordination Across Multi-Cluster Cloud Platforms

Multi-cluster cloud platforms introduce complex system-level resource management challenges in which actions taken within one cluster can propagate demand shifts, contention, and failure impacts across others. While cluster-local autoscaling and reactive controllers are effective for localized elasticity, they lack the global context required for coordinated capacity shaping, workload placement, and fairness across clusters operating under heterogeneous policies, network conditions, and regional constraints. This paper formulates multi-cluster resource optimization as a constrained, system-level control problem and presents a stability-aware federated coordination framework. The framework constructs a global system view from cross-cluster telemetry, computes admissible allocation and placement actions under policy, quota, and cost constraints, and regulates actuation using bounded step sizes and damping to ensure predictable convergence and avoid oscillatory behavior. A Kubernetes-based prototype deployed across geographically distributed clusters demonstrates reduced cross-cluster load skew, improved fairness, and faster system-level stabilization compared to cluster-isolated control, while maintaining bounded coordination overhead. These results underscore the necessity of stability-aware system-level coordination for reliable and efficient operation of large-scale multi-cluster cloud environments.