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.