A Closed/Open-Loop cache update strategy by peeking into the future

We consider a scenario where predictions of future content popularity are available to the cache manager. Our aim is to demonstrate how to optimally exploit the content popularity predictions to derive the cache update strategy that minimizes the traffic at the core network. We formulate our cache update problem as a Closed/Open-Loop (COL) one, which at each time decision instant takes into account the current users' content requests and the predicted future ones. In its original form, COL suffers from the curse of dimensionality. We then find a necessary and sufficient condition under which COL simplifies drastically and only requires a short prediction horizon and a limited computational complexity. Numerical investigations suggest that COL policy outperforms any cache update algorithm that only relies on past statistics. Remarkably, COL policy is robust with respect to popularity prediction errors. This advocates the exploitation of content popularity predictions for cache management purposes, even when predictions are largely inaccurate.