Demand-Driven Server Deployment for Green Computing Power Networks: A Multi-Objective Hierarchical Optimization Approach

Computing Power Networks (CPNs) have become an essential network architecture for supporting emerging applications, where an efficient server deployment scheme is critical to meeting growing demands. However, existing studies on server deployment schemes overlook the significant spatiotemporal variations in renewable energy availability and electricity prices, and inadequately consider network paths and task characteristics, ultimately leading to suboptimal decisions. To bridge this gap, this paper proposes a demand-driven server deployment scheme enabled by a spatiotemporal task scheduling strategy. Firstly, for the task scheduling scheme, we leverage a demand response program to perform triple selection of computing resources, routing paths, and forwarding time. Then, for the server deployment scheme, we obtain the computing resource demand of each CPN node based on the optimized scheduling decisions and further select energy-efficient servers with low procurement costs. Due to the interdependence between the scheduling and deployment schemes, a Multi-Objective Evolutionary Algorithm (MOEA)-based hierarchical solution is developed to iteratively find the optimal deployment solution. Simulation results show that the proposed scheme significantly reduces carbon emissions and annual costs while increasing the proportion of green energy usage, outperforming benchmark methods. The findings demonstrate the effectiveness of integrating scheduling and deployment for building efficient and sustainable CPN infrastructures.