Abstract— Predictive monitoring on distributed critical
infrastructures (DCI) is the ability to anticipate events that will
likely occur in the DCI before they actually appear, improving
the response time to avoid the rise of critical incidents.
Distributed into a region or country, DCIs such as smart grids or
microgrids rely on IoT, edge-fog continuum computing and the
growing capabilities of distributed application architectures to
collect, transport, and process data generated by the
infrastructure. We present a model-agnostic distributed
architecture for the inference execution of machine learning
window-based prediction models of predictive monitoring
applications to be used in this context. This architecture
transports the events generated by the DCI using event streams
to be processed by a hierarchy of nodes holding predictive
models. It also handles the offloading of inferences from
resource-scarce devices at lower levels to the resourceful upper
nodes. Therefore, the timing requirements for setting predictions
before they occur are met.
Index Terms— distributed critical infrastructures, predictive
monitoring, machine learning models inference on the edge,
hierarchical distributed architecture, streams based architecture.