A roadmap on learning and reasoning for distributed computing continuum ecosystems

Abstract

A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination. Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of Edge- Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. In this work, we focus on the approaches that center on high-level, general strategies, like the Free Energy Principle and Global Neuronal Workspace theories. The goal of exploring these techniques is to introduce principles that can potentially help us build distributed systems able to jointly work on the whole computing continuum, from the Edge to the Cloud, with self-adapting capabilities, i.e., dealing with uncertainty and the need for generalization, which is currently an open issue.