Tuning hybrid distributed storage system digital twins by reinforcement learning

In this paper, we consider the problem of fine-tuning a discrete event simulator of distributed storage system by a neural network trained with reinforcement learning algorithms on real data. The simulator has a set of control parameters that affect its behaviour and can be tuned during the simulation. Variation of these parameters influences how realistic the simulation is. The problem of simulator tuning is equivalent to the discovery of an optimal control strategy that leads to sensible results. We investigate different optimization metrics and demonstrate the viability of the approach.