  We consider the classical uncoded caching problem from an online learning
point-of-view. A cache of limited storage capacity can hold $C$ files at a time
from a large catalog. A user requests an arbitrary file from the catalog at
each time slot. Before the file request from the user arrives, a caching policy
populates the cache with any $C$ files of its choice. In the case of a
cache-hit, the policy receives a unit reward and zero rewards otherwise. In
addition to that, there is a cost associated with fetching files to the cache,
which we refer to as the switching cost. The objective is to design a caching
policy that incurs minimal regret while considering both the rewards due to
cache-hits and the switching cost due to the file fetches. The main
contribution of this paper is the switching regret analysis of a Follow the
Perturbed Leader-based anytime caching policy, which is shown to have an order
optimal switching regret. In this pursuit, we improve the best-known switching
regret bound for this problem by a factor of $\Theta(\sqrt{C}).$ We conclude
the paper by comparing the performance of different popular caching policies
using a publicly available trace from a commercial CDN server.
