Topology-Aware Joint Control Plane Placement and Assignment for Resilient Hierarchical Cloud–Edge Networks

Hierarchical cloud–edge networks rely on distributed control planes to manage large-scale heterogeneous infrastructures, where controller placement and node assignment strongly affect latency, load balancing, and resilience. Existing methods typically decouple these decisions and provide limited guarantees under controller failures or topology constraints. We introduce a topology-aware joint optimization framework for controller placement and node assignment in hierarchical cloud–edge networks. The problem is formulated as a multi-objective integer linear program capturing latency, load balancing, and control continuity. To ensure scalability, we design a two-phase heuristic: structurally important controller candidates are selected using graph-based metrics, including node degree and k-core decomposition, followed by a redundancy-aware proximity assignment strategy that preserves connectivity under single-controller failures. Experiments on synthetic hierarchical and random topologies with up to 500 nodes show that the proposed approach achieves optimality gaps below 10% with execution times under 10 ms. It improves load distribution and reduces control latency compared to baseline methods while maintaining resilience under controller failures. Results show that exploiting topological structure in joint placement and assignment enables efficient and resilient control plane design for hierarchical cloud–edge networks, supporting near-real-time reconfiguration.