The number of Single Board Computers (SBCs) is increasing, and so is the cumulative energy consumed by this category of device. Moreover, such devices are often always-on or running on batteries. Therefore, it is worth investigating their energy consumption to provide software developers and users with indicators for understanding how much energy the device is consuming while running a software application. In this paper, we explain a procedure for the creation of an energy consumption model of SBCs based on the usage of its components. We apply the procedure on a Raspberry PI 2 model B to test the model with a set of real applications. The results demonstrate the practical feasibility of the approach and show that estimated consumption values on our device have an average error of 2.2%, which is a good approximation without using external and expensive measuring devices.
Creating and Evaluating a Software Power Model for Linux Single Board Computers / Ardito, L.; Torchiano, M.. - ELETTRONICO. - (2018), pp. 1-8. (Intervento presentato al convegno 2018 ACM/IEEE 6th International Workshop on Green And Sustainable Software tenutosi a Gothenburg, Sweden nel 27 May 2018).
Creating and Evaluating a Software Power Model for Linux Single Board Computers
L. Ardito;M. Torchiano
2018
Abstract
The number of Single Board Computers (SBCs) is increasing, and so is the cumulative energy consumed by this category of device. Moreover, such devices are often always-on or running on batteries. Therefore, it is worth investigating their energy consumption to provide software developers and users with indicators for understanding how much energy the device is consuming while running a software application. In this paper, we explain a procedure for the creation of an energy consumption model of SBCs based on the usage of its components. We apply the procedure on a Raspberry PI 2 model B to test the model with a set of real applications. The results demonstrate the practical feasibility of the approach and show that estimated consumption values on our device have an average error of 2.2%, which is a good approximation without using external and expensive measuring devices.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2708872
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