  Dynamic load balancing lies at the heart of distributed caching. Here, the
goal is to assign objects (load) to servers (computing nodes) in a way that
provides load balancing while at the same time dynamically adjusts to the
addition or removal of servers. One essential requirement is that the addition
or removal of small servers should not require us to recompute the complete
assignment. A popular and widely adopted solution is the two-decade-old
Consistent Hashing (CH). Recently, an elegant extension was provided to account
for server bounds. In this paper, we identify that existing methodologies for
CH and its variants suffer from cascaded overflow, leading to poor load
balancing. This cascading effect leads to decreasing performance of the hashing
procedure with increasing load. To overcome the cascading effect, we propose a
simple solution to CH based on recent advances in fast minwise hashing. We
show, both theoretically and empirically, that our proposed solution is
significantly superior for load balancing and is optimal in many senses. On the
AOL search dataset and Indiana University Clicks dataset with real user
activity, our proposed solution reduces cache misses by several magnitudes.
