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

TL;DR

This paper examines how client-centric data consistency management affects energy consumption in large-scale storage systems like Apache HBase, aiming to inform energy-efficient design choices amidst growing data demands.

Contribution

It formalizes the relationship between data throughput, consistency, and energy footprint, providing insights into energy-efficient cluster storage system design.

Findings

Identifies key factors influencing energy consumption in HBase

Proposes a metric for trading consistency and energy efficiency

Provides lessons for designing energy-efficient storage clusters

Abstract

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.