A Survey on Computing Power Networks: Architecture, Resource Allocation, and AI Services Enablement

The rapid proliferation of large-scale artificial intelligence (AI) applications has led to unprecedented demand for distributed, efficient, and highly scalable computing infrastructures. Traditional communication networks have evolved to support cloud–edge–end collaborative computing. However, they remain fundamentally communication-centric and lack native mechanisms for coordinating widespread computing resources. Computing power networks (CPNs) address these limitations by adopting a computation-first design that treats computing power as a unified, schedulable network resource. CPNs enable on-demand computing resource scheduling. This allows heterogeneous tasks to be dynamically matched with the most suitable cloud, edge, or terminal nodes based on real-time workload conditions, device capabilities, and network states. Moreover, CPNs achieve deep computing–network collaboration through joint optimization of routing, task placement, data exchange, and resource orchestration, providing performance gains beyond traditional decoupled architectures. Their unified and programmable control framework further supports seamless integration across heterogeneous devices, enabling flexible resource virtualization and efficient management of diverse network environments. Motivated by these unique characteristics, this survey provides a comprehensive overview of CPN technologies, their architectural foundations, resource allocation mechanisms, and their emerging role in supporting large-scale distributed AI training and inference. We highlight key challenges, discuss open research problems, and provide insights into future directions for advancing computation-centric networked intelligence.