Authors:
Brian Setz
;
Faris Nizamic
;
Alexander Lazovik
and
Marco Aiello
Affiliation:
University of Groningen, Netherlands
Keyword(s):
Context Aware Power Management, Timeout Optimization, Green Computing, Energy Efficiency.
Related
Ontology
Subjects/Areas/Topics:
Algorithms for Reduced Power, Energy and Heat
;
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Green Computing Models, Methodologies and Paradigms
;
Optimization Techniques for Efficient Energy Consumption
;
Sustainable Computing and Communications
Abstract:
It has been shown that up to 64 percent of personal computers in office buildings are left running during after-hours.
Enabling power management options such as sleep mode is a straightforward method to reduce the
energy consumption of computers. However, choosing the right timeout can be challenging. A sleep timeout
which is too low leads to discomfort, whereas a timeout which is too high results in poor energy saving
efficiency. Having the users choose their own sleep timeout is not viable as research shows that most users
disable the sleep timeout completely, or choose a suboptimal timeout. Unlike existing context based power
management systems which use predefined rules, we propose a solution which can determine a personalized
sleep timeout for any point in time solely based on the users behaviour. We propose multiple models which
have the goal of maximizing the energy savings while minimizing discomfort. The models are tested on the
computers of employees of the University of Gr
oningen over several weeks. We analyse the results of the
experiments and determine which model performs best. We can potentially save between 4.02 and 17.17 kWh
per computer per year, depending on the model that used.
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