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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 doublesevery five years. On the other hand, computational developments have started tolag behind storage advancements, therein becoming a future bottleneck for theongoing data growth which already approaches Exascale levels. We investigatethe relationship among data throughput and energy footprint on a large storagecluster, with the goal of formalizing it as a metric that reflects the tradingamong consistency and energy. Employing a client-centric consistency approach,and while honouring ACID properties of the chosen columnar store for the casestudy (Apache HBase), we present the factors involved in the energy consumptionof the system as well as lessons learned to underpin further design ofenergy-efficient cluster scale storage systems.

On the energy efficiency of client-centric data consistency management under random read/write access to Big Data with Apache HBase