Equilibrium in the Computing Continuum through Active Inference
Boris Sedlak Victor Casamayor Pujol Praveen Kumar Donta Schahram Dustdar
Computing Continuum (CC) systems are challenged to ensure the intricate
requirements of each computational tier. Given the system's scale, the Service
Level Objectives (SLOs) which are expressed as these requirements, must be
broken down into smaller parts that can be decentralized. 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. Through
collaboration, they (3) increase the scope of SLO fulfillment. We implemented
the framework and evaluated a use case in which a CC system is responsible for
ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video
streaming. Our results showed that edge devices required only ten training
rounds to ensure four SLOs; furthermore, the underlying causal structures were
also rationally explainable. The addition of new types of devices can be done a
posteriori, the framework allowed them to reuse existing models, even though
the device type had been unknown. Finally, rebalancing the load within a device
cluster allowed individual edge devices to recover their SLO compliance after a
network failure from 22% to 89%.