System-Level Power Management Using Online Learning

In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve them using our algorithm. The selection is done a...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2009-05, Vol.28 (5), p.676-689
Main Authors: Dhiman, G., Rosing, T.S.
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Central processing units
Convergence
Devices
Disk drives
Dynamic voltage frequency scaling
Dynamic voltage scaling
Dynamical systems
Dynamics
Energy consumption
Energy management
energy-performance trade-off
Frequency
Hard disks
Learning
Management
online learning
Performance evaluation
Policies
Power management
Power system management
Stochastic processes
Workload
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Summary:In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve them using our algorithm. The selection is done among a set of experts, which refers to a set of DPM policies and voltage-frequency settings, leveraging the fact that different experts outperform each other under different workloads and device leakage characteristics. The online-learning algorithm adapts to changes in the characteristics and guarantees fast convergence to the best-performing expert. In our evaluation, we perform experiments on a hard disk drive (HDD) and Intel PXA27x core (CPU) with real-life workloads. Our results show that our algorithm adapts really well and achieves an overall performance comparable to the best-performing expert at any point in time, with energy savings as high as 61% and 49% for HDD and CPU, respectively. Moreover, it is extremely lightweight and has negligible overhead.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2009.2015740