  This paper studies an online replication problem for distributed data access.
The goal is to dynamically create and delete data copies in a multi-server
system as time passes to minimize the total storage and network cost of serving
access requests. We study the problem in the emergent learning-augmented
setting, assuming simple binary predictions about inter-request times at
individual servers. We develop an online algorithm and prove that it is
($\frac{5+\alpha}{3}$)-consistent (competitiveness under perfect predictions)
and ($1 + \frac{1}{\alpha}$)-robust (competitiveness under terrible
predictions), where $\alpha \in (0, 1]$ is a hyper-parameter representing the
level of distrust in the predictions. We also study the impact of
mispredictions on the competitive ratio of the proposed algorithm and adapt it
to achieve a bounded robustness while retaining its consistency. We further
establish a lower bound of $\frac{3}{2}$ on the consistency of any
deterministic learning-augmented algorithm. Experimental evaluations are
carried out to evaluate our algorithms using real data access traces.
