  Effective caching is crucial for the performance of modern-day computing
systems. A key optimization problem arising in caching -- which item to evict
to make room for a new item -- cannot be optimally solved without knowing the
future. There are many classical approximation algorithms for this problem, but
more recently researchers started to successfully apply machine learning to
decide what to evict by discovering implicit input patterns and predicting the
future. While machine learning typically does not provide any worst-case
guarantees, the new field of learning-augmented algorithms proposes solutions
that leverage classical online caching algorithms to make the machine-learned
predictors robust. We are the first to comprehensively evaluate these
learning-augmented algorithms on real-world caching datasets and
state-of-the-art machine-learned predictors. We show that a straightforward
method -- blindly following either a predictor or a classical robust algorithm,
and switching whenever one becomes worse than the other -- has only a low
overhead over a well-performing predictor, while competing with classical
methods when the coupled predictor fails, thus providing a cheap worst-case
insurance.
