Designing Reconfigurable Intelligent Systems with Markov Blankets
Boris Sedlak Victor Casamayor Pujol Praveen Kumar Donta Schahram Dustdar
Compute Continuum (CC) systems comprise a vast number of devices distributed
over computational tiers. Evaluating business requirements, i.e., Service Level
Objectives (SLOs), requires collecting data from all those devices; if SLOs are
violated, devices must be reconfigured to ensure correct operation. If done
centrally, this dramatically increases the number of devices and variables that
must be considered, while creating an enormous communication overhead. To
address this, we (1) introduce a causality filter based on Markov blankets (MB)
that limits the number of variables that each device must track, (2) evaluate
SLOs decentralized on a device basis, and (3) infer optimal device
configuration for fulfilling SLOs. We evaluated our methodology by analyzing
video stream transformations and providing device configurations that ensure
the Quality of Service (QoS). The devices thus perceived their environment and
acted accordingly -- a form of decentralized intelligence.