Control Aligned Source Role Forecasting for Proactive Edge Offloading

Accurate workload prediction does not necessarily translate into effective proactive offloading because forecast errors affect controller roles differently. This study develops an oracle-intervention framework to examine the decision value of event information, continuous magnitude, and source and destination forecasts within a fixed replay controller. Development-stage diagnostics motivate a lightweight hurdle predictor that separates a threshold-event score from conditional excess magnitude. The event score gates an upward source correction, while destination capacity remains tied to the current state. A data audit identifies future-dependent interpolation in the historical Alibaba evaluation; a post-review reanalysis using causal forward filling retains a modest overload benefit with additional migration work. A supplementary Azure invocation workload provides a second workload family but does not establish superiority over classical forecasts. Historical confirmation, representation ablations, and subsequent sensitivity analyses are explicitly distinguished. The contribution is a diagnostic methodology and a baseline-preserving deployment interface whose utility depends on workload, preprocessing, and migration cost. Cross-controller and real edge-deployment generalization remain open.