Comparing algorithm for dynamic speed-setting of a low-power CPU
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
Improving dynamic voltage scaling algorithms with PACE
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Automatic performance setting for dynamic voltage scaling
Proceedings of the 7th annual international conference on Mobile computing and networking
Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Job scheduling and data replication on data grids
Future Generation Computer Systems
Reducing network energy consumption via sleeping and rate-adaptation
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Communications of the ACM
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
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
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Distributed computing allows users to better utilize and share resources. Having this advantage currently requires a large amount of energy. In this paper we propose a solution for reducing power consumption in distributed computing, called Limited Reactive Power Management (LRPM). By monitoring the current usage of nodes, our solution will power down unnecessary nodes to conserve energy. This allows for power savings with minimal impact on performance. The overall goal is to obtain a lower energy consumption requirement by various distributed systems without sacrificing computing performance.