Calibrating parameters and formulas for process-level energy consumption profiling in smartphones
Battery-powered mobile devices substantially constrain energy resources. Process-level energy profiling tools can identify the most energy-consuming process and detail the energy usage of hardware components. With the help of energy profiling tools, programmers can fine-tune the energy consumption of processes to extend battery lifetime. However, profiling tools are highly dependent on hardware and must be calibrated for each hardware platform. Furthermore, for any new hardware components, new energy estimation formulas must be created. To solve these two problems regarding off-the-shelf products, this work proposes a two-phase calibrating approach. The first phase reconstructs the power table with a power meter, while the second creates new energy estimation formulas using linear regression analysis. The accuracy of the calibrated tool was evaluated in five scenarios and its error ratio is proven to be below 10%, occasionally less than 5%. Hence, this proposed approach to energy consumption profiling represents a major step in off-the-shelf devices.
energy profiling energy estimation calibration embedded system android