A predictive system shutdown method for energy saving of event-driven computation
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Dynamic power management for non-stationary service requests
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Dynamic power management based on continuous-time Markov decision processes
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Stochastic modeling of a power-managed system: construction and optimization
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Dynamic power management using adaptive learning tree
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
Quantitative comparison of power management algorithms
DATE '00 Proceedings of the conference on Design, automation and test in Europe
System level online power management algorithms
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Dynamic power management for portable systems
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Comparing System-Level Power Management Policies
IEEE Design & Test
Dynamic Power Management for Nonstationary Service Requests
IEEE Transactions on Computers
Machine Learning
Hierarchical Adaptive Dynamic Power Management
IEEE Transactions on Computers
Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management
HPCA '04 Proceedings of the 10th International Symposium on High Performance Computer Architecture
Policy optimization for dynamic power management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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In this article, a wavelet-based dynamic power management policy (WBDPM) is proposed. In this approach, the workload source (service requester) is modeled by a nonstationary time series which, in turn, represented by a nondecimated Haar wavelet as its basis. The proposed approach is robust and has the ability to minimize energy dissipation under different performance constraints. To assess the accuracy of the model, the algorithm was implemented for data extracted from the hard disks of computers. Prediction results of this approach for the case of a nonstationary service requester exhibit accuracies of more than 95%.