Abstract
Summary
In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model–based estimation owing to their architectural designs. Moreover, it further classifies code analysis–based modeling and estimation schemes in simulation‐based and profiling‐based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low‐accurate, resource expensive, or non‐scalable, as they consider marginally accurate smart battery's voltage/current sensors, low‐rate power capturing tools, and labor‐driven lab‐setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model–based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly.
As shown in figure, this paper discusses energy estimation methods and techniques for energy estimation of smartphone applications. It estimates energy consumption of applications based on smartphone components power models or source code energy models. It proposes taxonomies and highlights open research issues. It concludes that energy estimation is a resource expensive task owing to high profiling overhead.