Electrical Power Optimization of Cloud Data Centers Using Federated Learning Server Workload Allocation
Ashkan Safari, Afshin Rahimi · Electronics · 2025
Cloud Data Centers (CDCs) are the foundation of the digital economy, enabling data storage, processing, and connectivity for different academia/industry/commerce activities and digital services worldwide. As a result, their consistent power supply and reliable performance are critical factors; however, few works have considered power consumption optimization based on intelligent workload allocation. To this end, the proposed paper presents a Federated Learning (FL)-based server workload allocation model for optimal power optimization. In this strategy, the servers are modeled based on their Central Processing Unit (CPU), memory, storage, and network usage. A global server is considered as the global model responsible for final workload allocation decisions. Each server acts as a client in the federated learning framework, sharing its derived parameters with the global model securely and federatedly. Finally, after ten epochs of the system running, the model could optimize the system, decrease the overall power consumption, and reduce the workload pressure in each server by distributing it to other servers. The model is evaluated using different Key Performance Indicators (KPIs), and an appendix is provided, including the full performance results, workload shifting logs, and server resource status. Overall, the suggested FL allocator model shows promise in significantly lowering power consumption and alleviating server workload efficiently.