  This paper considers a variant of the online paging problem, where the online
algorithm has access to multiple predictors, each producing a sequence of
predictions for the page arrival times. The predictors may have occasional
prediction errors and it is assumed that at least one of them makes a sublinear
number of prediction errors in total. Our main result states that this
assumption suffices for the design of a randomized online algorithm whose
time-average regret with respect to the optimal offline algorithm tends to zero
as the time tends to infinity. This holds (with different regret bounds) for
both the full information access model, where in each round, the online
algorithm gets the predictions of all predictors, and the bandit access model,
where in each round, the online algorithm queries a single predictor. While
online algorithms that exploit inaccurate predictions have been a topic of
growing interest in the last few years, to the best of our knowledge, this is
the first paper that studies this topic in the context of multiple predictors
for an online problem with unbounded request sequences. Moreover, to the best
of our knowledge, this is also the first paper that aims for (and achieves)
online algorithms with a vanishing regret for a classic online problem under
reasonable assumptions.
