Automatic performance setting for dynamic voltage scaling
Proceedings of the 7th annual international conference on Mobile computing and networking
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Evaluating Scheduling and Replica Optimisation Strategies in OptorSim
GRID '03 Proceedings of the 4th International Workshop on Grid Computing
Latency-Driven Replica Placement
SAINT '05 Proceedings of the The 2005 Symposium on Applications and the Internet
Complete and fragmented replica selection and retrieval in Data Grids
Future Generation Computer Systems
A new paradigm: Data-aware scheduling in grid computing
Future Generation Computer Systems
Computer
Low power mode in cloud storage systems
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Toward energy-efficient computing
Communications of the ACM
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
Power-Saving in Large-Scale Storage Systems with Data Migration
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Data Replication and Power Consumption in Data Grids
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
PFRF: An adaptive data replication algorithm based on star-topology data grids
Future Generation Computer Systems
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Regardless of whether data is stored in a cluster, grid, or cloud, data management is being recognized as a significant bottleneck. Computing elements can be located far away from the data storage elements. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. In order to address such issues, we are designing and analyzing a series of energy efficient data aware strategies involving data replication and CPU scheduling. In this paper, we present a new strategy for data replication, called Queued Least-Frequently-Used (QLFU), and study its performance to determine if it is an energy efficient strategy. We also study the benefits of using a data aware CPU scheduling strategy, called data backfilling, which uses job preemption in order to maximize CPU usage and allows for longer periods of suspension time to save energy. We measure the performance of QLFU and existing replica strategies on a small green cluster to study the running time and power consumption of the strategies with and without data backfilling. Results from this study have demonstrated that energy efficient data management can reduce energy consumption without negatively impacting response time.