Performance-Based Guidelines for Energy Efficient Mobile Applications

Abstract

Mobile and wearable devices are nowadays the de facto personal computers, while desktop computers are becoming less popular. Therefore, it is important for companies to deliver efficient mobile applications. As an example, Google has published a set of best practices to optimize the performance of Android applications. However, these guidelines fall short to address energy consumption. As mobile software applications operate in resource-constrained environments, guidelines to build energy efficient applications are of utmost importance. In this paper, we studied whether or not eight best performance-based practices have an impact on the energy consumed by Android applications. In an experimental study with six popular mobile applications, we observed that the battery of the mobile device can last up to approximately an extra hour if the applications are developed with energy-aware practices. This work paves the way for a set of guidelines for energy-aware automatic refactoring techniques.

Catalog of energy patterns for mobile applications

“…Release resources or services as soon as possible (such as wake locks, screen) (Liu et al, 2016;Banerjee and Roychoudhury, 2016;Cruz and Abreu, 2017;Pathak et al, 2012b).…”

Energy efficiency across programming languages: how do energy, time, and memory relate?

“…For instance, for mobile applications, there are works focused on analyzing the energy efficiency of code blocks [5,19], or just monitoring how energy consumption evolves over time [11]. Other studies aimed at a more extensive energy consumption analysis, by comparing the energy efficiency of similar programs in specific usage scenarios [4,17], or by providing conclusions on the energy impact of different implementation decisions [7]. Several other works have shown that several factors, such as different design patterns [22,31], coding practices [21,26,29,32], and data structures [15,23,24,27], actually have a significant influence in the software's energy efficiency.…”

On the Energy Footprint of Mobile Testing Frameworks

“…As expected from previous work and corroborated with the violin plots, our measurements follow a normal distribution A n d ro id V ie w C li e n t -also confirmed with the Shapiro-Wilk test. Thus, we assess the statistical significance of the mean difference of energy consumption between frameworks using the parametric Welch's t-test as used in previous work [16]. We apply the Benjamini-Hochberg procedure by correcting p-values with the number of times a given sample is used in different tests.…”