8,384 research outputs found
Towards an Efficient Context-Aware System: Problems and Suggestions to Reduce Energy Consumption in Mobile Devices
Looking for optimizing the battery consumption is
an open issue, and we think it is feasible if we analyze the
battery consumption behavior of a typical context-aware
application to reduce context-aware operations at runtime.
This analysis is based on different context sensors
configurations. Actually existing context-aware approaches are
mainly based on collecting and sending context data to external
components, without taking into account how expensive are
these operations in terms of energy consumption. As a first
result of our work in progress, we are proposing a way for
reducing the context data publishing. We have designed a
testing battery consumption architecture supported by Nokia
Energy Profiler tool to verify consumption in different scenarios
Simple Energy Aware Scheduler: An Empirical Evaluation
Mobile devices have evolved from single purpose devices, such as mobile phone, into general purpose multi-core computers with considerable unused capabilities. Therefore, several researchers have considered harnessing the power of these battery-powered devices for distributed computing. Despite their ever-growing capabilities, using battery as power source for mobile devices represents a major challenge for applying traditional distributed computing techniques. Particularly, researchers aimed at using mobile devices as resources for executing computationally intensive task. Different job scheduling algorithms were proposed with this aim, but many of them require information that is unavailable or difficult to obtain in real-life environments, such as how much energy would require a job to be finished. In this context, Simple Energy Aware Scheduler (SEAS) is a scheduling technique for computational intensive Mobile Grids that only require easily accessible information. It was proposed in 2010 and it has been the base for a range of research work. Despite being described as easily implementable in real-life scenarios, SEAS and other SEAS-improvements works have always been evaluated using simulations. In this work, we present a distributed computing platform for mobile devices that support SEAS and empirical evaluation of the SEAS scheduler. This evaluation followed the methodology of the original SEAS evaluation, in which Random and Round Robin schedulers were used as baselines. Although the original evaluation was performed by simulation using notebooks profile instead of smartphones and tablets, results confirms that SEAS outperforms the baseline schedulers.Fil: PĂ©rez Campos, Ana Bella. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; ArgentinaFil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; Argentin
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