  The total estimated energy bill for data centers in 2010 was \$11.5 billion,
and experts estimate that the energy cost of a typical data center doubles
every five years. On the other hand, computational developments have started to
lag behind storage advancements, therein becoming a future bottleneck for the
ongoing data growth which already approaches Exascale levels. We investigate
the relationship among data throughput and energy footprint on a large storage
cluster, with the goal of formalizing it as a metric that reflects the trading
among consistency and energy. Employing a client-centric consistency approach,
and while honouring ACID properties of the chosen columnar store for the case
study (Apache HBase), we present the factors involved in the energy consumption
of the system as well as lessons learned to underpin further design of
energy-efficient cluster scale storage systems.
