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Application-Specific Energy Management in Operating Systems
- Thomas Weinlein
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- Advisor: Andreas Weißel, Dr.-Ing. F. Bellosa
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- Registered as Diplomarbeit DA-I4-2005-01 , January 17th 2005
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[Abstract]
[Full Paper (pdf) , 557 kB]
Power management is recognized as an important research area for mobile devices,
embedded systems and general purpose systems. There are several methods
for reducing the energy consumption of individual components and of the whole
system. But known methods often have the shortcoming that energy savings cause
performance reduction. Therefore power management has to adapt to the applications
and users performance demands. However there is a trade-off between the
users performance demands and energy savings. This work presents an approach
to automatically identify the performance demands of applications at runtime and
this way guiding the power management policies.
This approach extends the student thesis of Matthias Faerber to system wide
power management and several shortcomings are addressed. In the previous work
the currently running application is identified; Here, application usage profiles are
classified because one application can have different usage characteristics depending
on its current job. Furthermore the heuristic classification done by Faerber is
replaced by a theoretically sound classification and training algorithm based on
Classification And Regression Trees.
The presented approach gives the user the possibility to specify his personal
minimal performance demands for different application usage profiles. Then multiple
resource usage characteristics are retrieved from the CPU, the wireless network
interface card and the hard disk for each application. Upon those usage
characteristics it is possible to identify the usage profile of the currently running
application and to dynamically apply the adequate user-defined power management
setting. To account usage statistics for each application, the abstraction of
resource containers is used. The mapping of resource usage characteristics to the
appropriate power management setting is done by a classification demon. This
classifier is trained by supervised learning with the Classification And Regression
Tree algorithm.
The proposed approach for adaptive power management was evaluated on an
iPAQ running a modified Linux kernel and the classification demon. Several applications
that are typical for such a mobile platform were tested. A rate of correct
classifications of the user-specific performance demands and the corresponding
power management settings of approximately 98% was achieved.
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