SCPEC: State-Conditioned Pattern-Expert Composition for Workload Forecasting

Workload forecasting in dynamic multi-tenant edge-cloud platforms is crucial for resource provisioning, application deployment, and service assurance. However, heterogeneity across nodes and workload dynamics within each node make accurate forecasting challenging. Recent studies have adopted unified forecasting models to handle both, but representative ones still rely on a prototype pool of stored historical patterns, which limits their generalization to evolving workloads. To address this limitation, we reformulate multi-node workload forecasting as a multi-view regression problem, in which each node’s workload state is jointly characterized through temporal, spectral, and static-context views, and reusable knowledge is selected conditioned on this state. Building on this formulation, we propose SCPEC, which replaces the historical prototype pool with state conditioned composition of learnable pattern experts, thereby improving both generalization and forecasting accuracy. Experiments on eight real-world workload, electricity, and traffic datasets show that SCPEC achieves the best or tied-best results on 15 of 16 MSE/MAE evaluation entries. Our code is publicly available at https://anonymous.4open.science/r/test-F170/.