Towards Energy-Efficient Database Cluster Design

TL;DR

This paper investigates the architectural design space of energy-efficient parallel database clusters, analyzing key parameters and proposing a model to address energy consumption challenges in large-scale data systems.

Contribution

It provides an experimental analysis of cluster design parameters and introduces a model to optimize energy efficiency in parallel DBMS clusters.

Findings

Identified key bottlenecks affecting energy efficiency

Explored the impact of hardware choices on energy consumption

Proposed a model to guide energy-efficient cluster design

Abstract

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.