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Several smart phones in the feature of Android market are still in development stage and accordingly it is growing rapidly due to its large open source developer community. Smart devices have still low potential computing power such as limited in memory capacity, CPU speed and battery power lifetime. The objective of this study is to manage smart m...
Citations
... Rumi et al. proposed a method that simultaneously uses Dynamic Voltage and Frequency Scaling (DVFS) and User Driven Frequency Scaling (UDFS) to gradually reduce the clock rate until the user feels uncomfortable [16]. Memon et al. proposed a method to kill unnecessary applications running in the background, contrary to our proposed method [17]. In the work of Ref. [18], a method was proposed to control the CPU clock rate of an Android smartphone according to the quality of service obtained by the application. ...
Reducing power consumption is one of the most important issues in smartphones, especially for the CPU, since it is one of the most power-consuming devices. Improving the user experience by increasing CPU processing performance are also important. There is a trade-off between reducing CPU power consumption and improving the user experience. Decreasing the CPU clock rate reduces power consumption but degrades the user experience. Increasing the clock rate does the opposite. Therefore, it is desirable to increase the clock rate when and only when CPU resource consumption is high, and decrease it when it is low. However, the kernels of many smartphone operating systems, including the Linux kernel in the Android OS, use a follow-up policy of increasing or decreasing the clock rate after observing an increase or decrease in CPU resource consumption, which does not immediately provide appropriate clock rate control. We believe that predicting CPU usage in the near future will be critical for proper control. In this paper, we focus on the Android OS and propose a method to predict CPU usage in the near future by observing the behavior of foreground applications, and controlling the CPU clock rate based on the prediction. The proposed method modifies Android Runtime, which is the application execution environment, observes application method calls in Android Runtime, and predicts CPU usage in the near future based on these observations. We then demonstrate the effectiveness of the proposed method using our microbenchmark application and an actual distributed Android application.
... The study by Hammad et al. [36] shows the way how smartphone mobile devices power consumption can be managed by killing unnecessary applications running in the background, and adjusting CPU frequency. The study uses task killer applications and setting CPU frequencies based on real load as method of investigation. ...
Android application is becoming critical in our daily lives by helping and enabling us to compute service intensive applications easily. These android applications run on smartphone mobile device which is relatively short of resources than computers. Android mobile applications require different approach for applications quality and efficient programming, analysis approach for better resource utilization such as memory and energy or battery, and for preventing and resolving resource leak behavior of apps. I have performed systematic literature review to categorize and to structure the research findings that has been published in the area of android application memory and energy performance, resource leaks, and performance testing techniques and challenges that they have reported. Thirty-one (31) empirical studies are mapped in to the classification scheme. Different research gaps that needs to be filled are identified and issues for practitioners are identified: there is a need to optimize memory and energy utilization; specific road-map or guideline to follow for application development, and studies on the issues of resource leaks due to various patterns, programming techniques, performance improvement, and source code analysis.
... Existing Android DVFS policies implemented through device-specific governors [16], such as interactive, ondemand and performance, primarily focus on QoS instead of system-wide energy consumption and, hence, fail to achieve an optimal energy-efficient policy when used in popular applications [12], [17]. In addition, existing solutions are applicable to only a small subset of popular Android applications [18], [19] or performance benchmarks and microbenchmarks, and were either tested on software simulators or open-source development boards [20], [21], [22]. ...
The split-screen mode in smartphones allows for the simultaneous side-by-side execution of multiple applications, which permits multitasking and improves users’ experience. However, such technology results in simultaneously running multiple foreground processes, which increases the power consumption of a smartphone and reduces its battery lifetime. We present an integrated system-level resource management framework that aims to minimize the total energy consumption of a smartphone with negligible impact on the quality of service (QoS) of applications whose resource usage characteristics are not precisely known offline or vary over time. Our proposed solution (1) leverages applications’ offline profiles to detect instantaneous phase changes (i.e., dynamic changes in resource usage patterns) of the workload of a given application at runtime, and (2) adaptively adjusts both the voltage and frequency settings of the processor and memory bandwidth to achieve the most energy-efficient configuration subject to QoS constraints. Our approach is also able to progressively reduce the energy consumption of newly installed real-world applications for which there exists no prior resource usage data. Experiments on a Nexus 6 smartphone show that our approach achieves an average energy reduction of 23% (19%) and up to 31% (27%) compared to existing work [1] (default Android governor) for different combinations of real-world applications running side-by-side in split-screen mode. For applications with no prior resource usage data, the proposed framework saves up to 22% (18%) of energy within at most 14 seconds when compared to existing work [1] (default Android governor).
