Application Characterization for Wireless Network Power Management
Andreas Weissel, Matthias Faerber, Frank Bellosa, "Application
Characterization for Wireless Network Power Management", Proceedings of the
International Conference on Architecture of Computing Systems (ARCS'04),
Augsburg, Germany, March 2004, Published in Organic and Pervasive Computing -
ARCS 2004, Lecture Notes in Computer Science 2981, Springer Verlag, March 2004,
pp. 231-245
[Abstract]
[Full Paper (pdf), 385 kB]
(c)
Springer-Verlag [Talk (pdf)]
Abstract:
The popular IEEE 802.11 standard defines a power saving mode that keeps the
network interface in a low power sleep state and periodically powers it up
to synchronize with the base station. The length of the sleep interval, the
so called beacon period, affects two dimensions, namely application
performance and energy consumption. The main disadvantage of this power
saving policy lies in its static nature: a short beacon period wastes
energy due to frequent activations of the interface while a long beacon
period can cause diminished application responsiveness and performance.
While the first aspect, reduction of power consumption, has been studied
extensively, the implications on application performance have received only
little attention. We argue that the tolerable reduction of performance or
quality depends on the application and the user. As an example, a beacon
period of only 100ms slows down RPC-based operations like NFS dramatically,
while the user will probably not recognize the additional delay when using
a web browser. If at all, known power management algorithms guarantee a
system wide limit on performance degradation without differentiating
between different application profiles.
This work presents an approach to identify on-line the currently running
application class by a mapping of network traffic characteristics to a
predefined set of application profiles. We propose a power saving policy
which dynamically adapts to the detected application profile, thus
identifying the application- and user-specific power/performance trade-off.
An implementation of the characterization algorithm is presented and
evaluated running several typical applications for mobile devices.