A survey on energy estimation and power modeling schemes for smartphone applications

Abstract

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. KEYWORDS application energy, energy estimation, energy profiling, profiling overhead 1 | INTRODUCTION Nowadays, with the proliferation of portable devices, the energy-efficient system design has become a must-to-meet requirement for recent resource-constrained smartphone devices. The ever growing smartphone user's demands encourage application developers to enrich the legacy applications that as a result, increase smartphone's computational and communication cost. Among all smartphone-based applications, on-demand video, 1 multi-agent-based distributed games, 2 spatial locality-based social applications, 3 pedestrian tracking, 4,5 and context-based advertisement services 6 are the most energy-hungry services. The inherent features of these services considerably increase the energy demands of processors when executed on smartphones. 7-9 It is estimated that in last 2 decades, processor power budget has increased significantly because of smartphone application's high resource use. To efficiently exploit smartphone's power budget, software optimization minimizes inter-component interaction among hardware modules that as a result, increases battery's lifetime. ...

Modeling the Total Energy Consumption of Mobile Network Services and Applications

“…The energy consumption of an application is highly dependent on the degree of interactions among different smartphone components during application execution process. Smartphone components such as 3G/4G, WiFi, CPU, LCD, Bluetooth, and GPS are the most energy consuming components [7]. In [8], the authors proposed an experimental framework for measuring the Android smartphone power consumption of web pages, including specific components on the page, such as cascade style sheets (CSS), Javascript, images, and plug-in objects.…”

“…Measuring the power consumption of smartphones requires the use of external power measurement tools or a self-metering power measurement methodology with different granularities [7,8]. The measurement procedures are discussed in the next section.…”

“…We demonstrate the process to acquire the measurements for the six major parameters of mobile services shown in Figure 2: data traffic T S_data (bps), signaling traffic T S_sig (bps), data duration D data (s), signaling duration D sig (s), CPU power consumption P S_CPU (W) and 4G connection power consumption P S_4G (W). These parameters are crucial in developing the end-to-end energy consumption model since service energy consumption of smartphones represents a major portion, yet existing research [7][8][9][10][11][12][13][14][15] has limited insights into this aspect. Therefore, we develop test cases for 15 different mobile services from seven mobile applications, as shown in Table 1.…”

“…For example, various power models have been developed for estimating the energy consumption of different smartphone components such as 3G/4G, WiFi, central processing unit (CPU), liquid crystal display (LCD) and global positioning system (GPS). The researches in [7][8][9][10] show that the energy consumption of smartphones is influenced by different traffic characteristics and signaling patterns of mobile applications. Various power models of a long-term evolution (LTE) BS proposed in [11][12][13][14][15] try to assess energy consumption of mobile applications by separating their total energy consumption into data and signaling energy components.…”

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