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Event Engine for Adaptive Mobile Computing
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DRPM: dynamic speed control for power management in server class disks
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Tracking the best linear predictor
The Journal of Machine Learning Research
Tracking linear-threshold concepts with Winnow
The Journal of Machine Learning Research
PB-LRU: a self-tuning power aware storage cache replacement algorithm for conserving disk energy
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Performance directed energy management for main memory and disks
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Power-Aware Storage Cache Management
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Performance directed energy management for main memory and disks
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Modeling Hard-Disk Power Consumption
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Efficient management of idleness in storage systems
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Adaptive timeout policies for fast fine-grained power management
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Power Modeling of Solid State Disk for Dynamic Power Management Policy Design in Embedded Systems
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Conserving energy in real-time storage systems with I/O burstiness
ACM Transactions on Embedded Computing Systems (TECS)
Autonomic exploration of trade-offs between power and performance in disk drives
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A reliability model of energy-efficient parallel disk systems with data mirroring
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Low power management of OLTP applications considering disk drive power saving function
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Modeling hard-disk power consumption
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Analysis of disk power management for data-center storage systems
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An adaptive energy-conserving strategy for parallel disk systems
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Potentiality of power management on database systems with power saving function of disk drives
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
Combining initial segments of lists
Theoretical Computer Science
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We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. One of the most critical resources in mobile computing environments is battery life, and good energy conservation methods increase the utility of mobile systems. We use a simple and efficient algorithm based on machine learning techniques that has excellent performance. Using trace data, the algorithm outperforms several methods that are theoretically optimal under various worst‐case assumptions, as well as the best fixed time‐out strategy. In particular, the algorithm reduces the power consumption of the disk to about half of the energy consumed by a one minute fixed time‐out policy. Furthermore, the algorithm uses as little as 88% of the energy consumed by the best fixed time‐out computed in retrospect.