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Equilibrium in the Computing Continuum through Active Inference
We present our framework for collaborative edge intelligence enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs, and (2) transfer knowledge to speed up the onboarding of heterogeneous devices.
Active Inference on the Edge: A Design Study
Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e. g., to optimize the task distribution between devices.
Designing Reconfigurable Intelligent Systems with Markov Blankets
Compute Continuum (CC) systems comprise a vast number of devices distributed over computational tiers.
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