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Author: search.filters.author.Chang, Shun-LeeSubject: search.filters.subject.Android

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    Calibrating parameters and formulas for process-level energy consumption profiling in smartphones
    (2014-09-01)
    Lin, Ying-Dar
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    Rattagan, Ekarat
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    Lai, Yuan-Cheng
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    Chang, Li-Pin
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    Yo, Yun-Chien
    Battery-powered mobile devices substantially constrain energy resources. Process-level energy profiling tools can identify the most energy-consuming process and detail the energy usage of hardware components. With the help of energy profiling tools, programmers can fine-tune the energy consumption of processes to extend battery lifetime. However, profiling tools are highly dependent on hardware and must be calibrated for each hardware platform. Furthermore, for any new hardware components, new energy estimation formulas must be created. To solve these two problems regarding off-the-shelf products, this work proposes a two-phase calibrating approach. The first phase reconstructs the power table with a power meter, while the second creates new energy estimation formulas using linear regression analysis. The accuracy of the calibrated tool was evaluated in five scenarios and its error ratio is proven to be below 10%, occasionally less than 5%. Hence, this proposed approach to energy consumption profiling represents a major step in off-the-shelf devices. (C) 2014 Elsevier Ltd. All rights reserved.
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    Booting, browsing and streaming time profiling, and bottleneck analysis on android-based systems
    (2013-07-01)
    Lin, Ying-Dar
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    Ho, Cheng-Yuan
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    Lai, Yuan-Cheng
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    Du, Tzu-Hsiung
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    Chang, Shun-Lee
    Android-based systems perform slowly in three scenarios: booting, browsing, and streaming. Time profiling on Android devices involves three unique constraints: (1) the execution flow of a scenario invokes multiple software layers, (2) these software layers are implemented in different programming languages, and (3) log space is limited. To compensate for the first and second constraints, we assumed a staged approach using different profiling tools applied to different layers and programming languages. As for the last constraint and to avoid generating enormous quantities of irrelevant log data, we began profiling scenarios from an individual module, and then iteratively profiled an increased number of modules and layers, and finally consolidated the logs from different layers to identify bottlenecks. Because of this iteration, we called this approach a staged iterative instrumentation approach. To analyze the time required to boot the devices, we conducted experiments using off-the-shelf Android products. We determined that 72% of the booting time was spent initializing the user-space environment, with 44.4% and 39.2% required to start Android services and managers, and preload Java classes and resources, respectively. Results from analyzing browsing performance indicate that networking is the most significant factor, accounting for at least 90% of the delay in browsing. With regard to online streaming, networking and decoding technologies are two most important factors occupying 77% of the time required to prepare a 22 MB video file over a Wi-Fi connection. Furthermore, the overhead of this approach is low. For example, the overhead of CPU loading is about 5% in the browsing scenario. We believe that this proposed approach to time profiling represents a major step in the optimization and future development of Android-based devices. (c) 2013 Elsevier Ltd. All rights reserved.