Extending lifetime of portable systems by battery scheduling
Proceedings of the conference on Design, automation and test in Europe
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Discharge Current Steering for Battery Lifetime Optimization
IEEE Transactions on Computers
IEEE Transactions on Parallel and Distributed Systems
Power-Aware Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Toward a Realistic Task Scheduling Model
IEEE Transactions on Parallel and Distributed Systems
Voluntary cooperation in pervasive computing services
LISA '05 Proceedings of the 19th conference on Large Installation System Administration Conference - Volume 19
A new strategy for multiprocessor scheduling of cyclic task graphs
International Journal of High Performance Computing and Networking
Scheduling battery usage in mobile systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Stochastic DFS for multiprocessor scheduling of cyclic taskgraphs
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
An approach to understanding policy based on autonomy and voluntary cooperation
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
Network patterns in cfengine and scalable data aggregation
LISA'07 Proceedings of the 21st conference on Large Installation System Administration Conference
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A grid-like environment may be constructed from ad hoc processing devices, including portable battery-powered devices. Battery lifetime is a current limitation here. In this paper we propose policies for minimizing power consumption using voluntary collaboration between the autonomously controlled nodes. We exploit the quadratic relationship between processor clock-speed and power consumption to identify processing devices which can be slowed down to save energy while maintaining an overall computational performance across a collaboration of nodes.