FedPC Cloud CPU Forecasting using Federated Learning and Periodicity-based Clustering

Accurate CPU workload forecasting is vital for efficient resource management and system availability in cloud computing, yet faces challenges in data privacy, security, and the "cold start"problem for new Virtual Machines (VMs). Traditional methods risk privacy and struggle with limited data. We propose FedPC, a novel framework leveraging privacy-preserving Federated Learning (FL) with Periodicity-based Clustering. FedPC enables collaborative training without exposing local data, crucially supporting effective forecasting for new VMs with minimal historical information. It clusters VMs by workload periodicity, training tailored LSTMs within each cluster to handle heterogeneity securely. Evaluated on Azure Public Dataset V1, FedPC surpasses FedAvg in privacy and matches state-of-the-art methods in accuracy. This demonstrates FedPC’s efficacy in securely, adaptively managing resources, thereby enhancing system availability, especially in dynamic cloud environments with frequent VM creation and scarce initial data.