Estimating Execution Progress of Black-Box Tasks with Resource-Consumption Models
This work presents a novel approach that, given a generic resource-consumption model, estimates the progress of an ongoing execution in the form of a probability distribution which supports uncertainty quantification, and performs estimation via Markov-chain Monte-Carlo sampling around a maximum a posteriori estimate.