An Empirical Study of Power Consumption of Web-Based Communications in Mobile Phones

Abstract

Abstract-Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.

An Energy Efficiency Study of Web-Based Communication in Android Phones

“…is work is part of our ongoing study on the energy consumption of asynchronous communication mechanisms. is work extends our previous contribution [12] with a new experimental set and a new kind of experiment (i.e., longer interaction experiments). Additionally, we make the web browser part of our study and analyse whether there is a statistically significant difference on the power consumption of the mechanisms.…”

“…In a previous contribution [12], we have used both GreenOracle and Trepn Profiler tools to profile the power consumption of different Android devices. As the results of the experiments were remarkably similar for all the devices and profiling tools, in this contribution, we have opted to use just one of the tools.…”

“…e energy consumption in mobile devices has been studied in different works. e experimental studies cope with energy consumption in different contexts and with different purposes [12,27,[29][30][31][39][40][41][42][43][44][45][46][47][48][49][50][51]. ese studies are conducted to help developers about being concerned of energy consumption and contribute to provide alternative choices more energy saving.…”

Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research

“…To perform an energy measurement, the mobile device is typically set up in a controlled test environment in which the conditions are held as good as possible constant, and only a few parameters, like connection parameters (e.g., [31], [51], [52]) or cache type (e.g., [3], [33], [39]), are adopted. In the analysed dataset, we have identified common principles that we want to discuss further to obtain comprehensible test results.…”

“…[3], [16], [26], [27], [30], [31], [31]- [34], [39], [43], [46], [47], [49], [50], [59] Hardware-based 24 [14], [15], [28], [29], [35], [37], [38], [40], [41], [44], [45], [51]- [56], [58], [60], [62]- [65], [67] Estimation-based 4 [36], [42], [48], [61] supplier profiles) on the device and can be accessed via the Android Debug Bridge, for further analysis, even on a single hardware unit [30], [32], [33]. An alternative for devices with Qualcomm chips was Trepn Profiler [31], [32], [49], [50]. It was also used by Ahmad et al [70] with the PowerTutor app to evaluate the performance of dynamic analysis-based energy estimation.…”