Predictability of Process Resource Usage: A Measurement-Based Study on UNIX
IEEE Transactions on Software Engineering
SUIF: an infrastructure for research on parallelizing and optimizing compilers
ACM SIGPLAN Notices
A dynamic disk spin-down technique for mobile computing
MobiCom '96 Proceedings of the 2nd 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
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Scheduler-based DRAM energy management
Proceedings of the 39th annual Design Automation Conference
Condor-G: A Computation Management Agent for Multi-Institutional Grids
Cluster Computing
Using Disk Throughput Data in Predictions of End-to-End Grid Data Transfers
GRID '02 Proceedings of the Third International Workshop on Grid Computing
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
The Impact of More Accurate Requested Runtimes on Production Job Scheduling Performance
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
Adaptive Disk Spin-down Policies for Mobile Computers
MLICS '95 Proceedings of the 2nd Symposium on Mobile and Location-Independent Computing
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Run-Time Statistical Estimation of Task Execution Times for Heterogeneous Distributed Computing
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Minimizing energy for wireless web access with bounded slowdown
Wireless Networks
Integrated Data Reorganization and Disk Mapping for Reducing Disk Energy Consumption
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
A Quantitative analysis of disk drive power management in portable computers
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
lmbench: portable tools for performance analysis
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
On the User-Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions
International Journal of High Performance Computing Applications
Modeling job arrivals in a data-intensive grid
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Profile-based optimization of power performance by using dynamic voltage scaling on a PC cluster
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Assessing Green Strategies in Peer-to-Peer Opportunistic Grids
Journal of Grid Computing
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Energy usage of Grid has become critical due to environmental, cost and heat factors. Significant amounts of energy can be saved by effective transition to lower power states. We present an energy aware scheduler for Desktop Grid environment. Considering that task performance prediction is the key in such scenarios, we present a hardware prediction tool to model the user application. Memory wall problem being the biggest bottleneck in today's applications, we apply energy scheduling to memory intensive tasks. We model the complete system considering all devices like processor, hard drive and various controllers. For static program analysis in this work, we have chosen memory intensive applications commonly used in scientific applications. Our experiments show that significant amount of energy can be saved with little performance degradation. Normally, the user highly overestimates the job execution time. Our prediction tool based on static program analysis estimates the task execution time to within 1--30% error. The overall energy savings falls in the range of 12--45% based on the standard workloads and different Grid scenarios for the given devices in the system.