Parallel database systems: the future of high performance database systems
Communications of the ACM
Communications of the ACM - Voting systems
STEP: Self-Tuning Energy-safe Predictors
Proceedings of the 6th international conference on Mobile data management
Performance tradeoffs in read-optimized databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Database servers tailored to improve energy efficiency
SETMDM '08 Proceedings of the 2008 EDBT workshop on Software engineering for tailor-made data management
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Tracking the power in an enterprise decision support system
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
FAWN: a fast array of wimpy nodes
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Proceedings of the VLDB Endowment
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
On the energy (in)efficiency of Hadoop clusters
ACM SIGOPS Operating Systems Review
Low-power amdahl-balanced blades for data intensive computing
ACM SIGOPS Operating Systems Review
Energy-efficient cluster computing with FAWN: workloads and implications
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Robust and flexible power-proportional storage
Proceedings of the 1st ACM symposium on Cloud computing
Analyzing the energy efficiency of a database server
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On energy management, load balancing and replication
ACM SIGMOD Record
FAWNdamentally power-efficient clusters
HotOS'09 Proceedings of the 12th conference on Hot topics in operating systems
Delivering energy proportionality with non energy-proportional systems: optimizing the ensemble
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Wimpy node clusters: what about non-wimpy workloads?
Proceedings of the Sixth International Workshop on Data Management on New Hardware
Energy management for MapReduce clusters
Proceedings of the VLDB Endowment
A case for micro-cellstores: energy-efficient data management on recycled smartphones
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Energy efficiency for large-scale MapReduce workloads with significant interactive analysis
Proceedings of the 7th ACM european conference on Computer Systems
Energy-proportional query execution using a cluster of wimpy nodes
Proceedings of the Ninth International Workshop on Data Management on New Hardware
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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.