Active Inference in the Distributed Computing Continuum

Distributed applications now span sensors, edge nodes, fog clusters, and hyperscale clouds.Meeting service-level objectives across this "Distributed Computing Continuum" persistently fails when management is reactive, centralized, and blind to uncertainty.I argue for predictive equilibrium as the control objective and for a concrete diagnostic: the Kullback-Leibler divergence between a system's expected and observed causal behavior under perturbations, each modeled with a Bayesian network.This perspective draws from predictive regulation in neuroscience and the fluctuation-dissipation view of equilibrium in physics, and it sets the stage for antifragilitysystems that get better because they were stressed, not despite it.