Context-aware power management
Citation:
Colin Harris, 'Context-aware power management', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2007, pp 316Download Item:
Abstract:
With more and more computing devices being deployed in buildings there has been a steady rise in
buildings’ electricity consumption. These devices not only consume electricity but also produce heat,
which increases loading on ventilation systems, further increasing electricity consumption. At the
same time there is a pressing need to reduce overall building energy consumption. For example, the
European Union’s strategy for security of energy supply highlights energy saving in buildings as a
key target area. One approach to reducing energy consumption of devices in buildings is to improve
the effectiveness of their power management.
Current state-of-the-art computer power management is predominantly focused on extending battery
life for mobile computing devices. The majority of policies are low-level and are used to manage
sub-components within the overall computing device. The key trade-off for these policies is device
performance versus increased battery life. In contrast, stationary computing devices do not have battery
limitations and typically the most significant energy savings are achieved by switching the entire
device to standby. However, switching to a deep standby state can cause significant user annoyance
due to the relatively long resume time and possible false power downs. Consequently these energy
saving features are typically not enabled (or used with long timeouts). To increase enablement, policies
for stationary devices need to operate in a near transparent fashion, i.e., operate automatically
and with little user-perceived performance degradation.
Context-aware pervasive computing describes a vision of computing everywhere that seamlessly
assists us in our daily tasks, i.e., many functions are intelligently automated. Information display,
computing, sensing and communication will be embedded in everyday objects and within the environment’s
infrastructure. Seamless interaction with these devices will enable a person to focus on their
task at hand while the devices themselves vanish into the background. Realisation of this vision could
exacerbate the building energy problem as more stationary computing devices are deployed but it
could also provide a solution. Context information (e.g., user location information) likely to be available
in such pervasive computing environments could enable highly effective power management for many of a building’s electricity consuming devices. We term such power management techniques as
context-aware power management (CAPM), their principal objective being to minimise overall electricity
consumption while maintaining user-perceived device performance. The current state of the
art in context-aware computing focuses on developing inference techniques for determining high-level
context from low-level, noisy, and incomplete sensor data. Possible approaches include rule-based
inference, Bayesian inference, fuzzy control, and hidden Markov models. Successful inference enables
the vision of computing services interfacing seamlessly and transparently with users’ daily tasks. One such desirable, transparent service is context-aware power management.
We have identified several key requirements and designed a framework for CAPM. At the core
of the framework, a Bayesian inference technique is employed to infer relevant context from a given range of sensors. We have identified the principal context required for effective CAPM as being (i)
when the user is not using and (ii) when the user is about to use a device. Accurately inferring this user context is the most challenging part of CAPM. However, there is also a balance between how much energy additional context can save and how much it will cost both monetarily and energy
wise. To date there has been some research in the area of CAPM but to our knowledge there has been no detailed study as to what granularity of context is appropriate and what are the potential energy savings.
We have conducted an extensive user study to empirically answer these questions for CAPM of
desktop PCs in an office environment. The sensors used are keyboard/mouse input, user presence
based on Bluetooth beaconing, near presence based on ultrasonic range detection, face detection, and
voice detection. Results from the study show that there is wide variability of usage patterns and
that there is a balance whereby adding more sensors actually increases the energy consumption. For
the desktop PC study, idle time, user presence, and near presence are sufficient for effective power
management coming within 6-9% of the theoretical optimal policy (on average). Beyond this face
detection and voice detection consumed more than they saved. The evaluation further demonstrates
the use of Bayesian inference as a viable technique for CAPM.
Author: Harris, Colin
Advisor:
Cahill, VinnyPublisher:
Trinity College (Dublin, Ireland). School of Computer Science & StatisticsNote:
TARA (Trinity's Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ieType of material:
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