<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css">

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

Filters

20131

Settings

🏛️國立陽明交通大學機構典藏

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

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item type:Item,
    Booting, browsing and streaming time profiling, and bottleneck analysis on android-based systems
    (2013-07-01)
    Lin, Ying-Dar
    ;
    Ho, Cheng-Yuan
    ;
    Lai, Yuan-Cheng
    ;
    Du, Tzu-Hsiung
    ;
    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.