  Modern high load applications store data using multiple database instances.
Such an architecture requires data consistency, and it is important to ensure
even distribution of data among nodes. Load balancing is used to achieve these
goals.
  Hashing is the backbone of virtually all load balancing systems. Since the
introduction of classic Consistent Hashing, many algorithms have been devised
for this purpose.
  One of the purposes of the load balancer is to ensure storage cluster
scalability. It is crucial for the performance of the whole system to transfer
as few data records as possible during node addition or removal. The load
balancer hashing algorithm has the greatest impact on this process.
  In this paper we experimentally evaluate several hashing algorithms used for
load balancing, conducting both simulated and real system experiments. To
evaluate algorithm performance, we have developed a benchmark suite based on
Unidata MDM~ -- a scalable toolkit for various Master Data Management (MDM)
applications. For assessment, we have employed three criteria~ -- uniformity of
the produced distribution, the number of moved records, and computation speed.
Following the results of our experiments, we have created a table, in which
each algorithm is given an assessment according to the abovementioned criteria.
