  Energy is a growing component of the operational cost for many "big data"
deployments, and hence has become increasingly important for practitioners of
large-scale data analysis who require scale-out clusters or parallel DBMS
appliances. Although a number of recent studies have investigated the energy
efficiency of DBMSs, none of these studies have looked at the architectural
design space of energy-efficient parallel DBMS clusters. There are many
challenges to increasing the energy efficiency of a DBMS cluster, including
dealing with the inherent scaling inefficiency of parallel data processing, and
choosing the appropriate energy-efficient hardware. In this paper, we
experimentally examine and analyze a number of key parameters related to these
challenges for designing energy-efficient database clusters. We explore the
cluster design space using empirical results and propose a model that considers
the key bottlenecks to energy efficiency in a parallel DBMS. This paper
represents a key first step in designing energy-efficient database clusters,
which is increasingly important given the trend toward parallel database
appliances.
