  Most cloud services and distributed applications rely on hashing algorithms
that allow dynamic scaling of a robust and efficient hash table. Examples
include AWS, Google Cloud and BitTorrent. Consistent and rendezvous hashing are
algorithms that minimize key remapping as the hash table resizes. While memory
errors in large-scale cloud deployments are common, neither algorithm offers
both efficiency and robustness. Hyperdimensional Computing is an emerging
computational model that has inherent efficiency, robustness and is well suited
for vector or hardware acceleration. We propose Hyperdimensional (HD) hashing
and show that it has the efficiency to be deployed in large systems. Moreover,
a realistic level of memory errors causes more than 20% mismatches for
consistent hashing while HD hashing remains unaffected.
