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  • PhD Defense -Yixue Zhao

    Fri, Oct 16, 2020 @ 09:00 AM - 11:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Yixue Zhao



    Committee:

    Nenad Medvidovic (Chair)

    Chao Wang

    Bhaskar Krishnamachari



    Date: 10/16/2020

    Time: 9am

    Zoom: https://usc.zoom.us/j/96796759326?pwd=aTF3SnJlS3ljM1pjMkhZNzIyVGttdz09
    Meeting ID: 967 9675 9326
    Passcode: 149878



    Title: Reducing User-Perceived Latency in Mobile Applications via Prefetching and Caching



    Prefetching and caching is a fundamental approach to reduce user-perceived latency, and has been shown effective in various domains for decades. However, its application on today's mobile apps remains largely under-explored. This is an important but overlooked research area since mobile devices have become the dominant platform, and this trend is reflected in the billions of mobile devices and millions of mobile apps in use today. At the same time, user-perceived latency has been shown to have a large impact on mobile-user experience and can cause significant economic consequences.

    In my dissertation, I aim to fill this gap by providing a multifaceted solution to establish the foundation for exploring various aspects of prefetching and caching techniques in the mobile-app domain. To that end, my dissertation consists of four major elements. As a first step, I conducted an extensive study to investigate the opportunities for applying prefetching and caching techniques in mobile apps, providing empirical evidence on their applicability and showing insights to guide future techniques. Second, I developed PALOMA, the first content-based prefetching technique for mobile apps using program analysis, which has achieved significant latency reduction with high accuracy and negligible overhead. Third, I constructed HiPHarness, a tailorable framework for investigating history-based prefetching in a wide range of scenarios. Guided by today's stringent privacy regulations that have limited the access to mobile-user data, I further leveraged HiPHarness to conduct the first study on history-based prefetching with "small" prediction models, demonstrating its feasibility on mobile platforms and in turn, opening up a new research area. Finally, to reduce the manual effort required in evaluating prefetching and caching techniques, I have devised FrUITeR, a framework for assessing test-reuse techniques in order to automatically select suitable test cases to evaluate prefetching and caching techniques, without real users' engagement as required previously.

    WebCast Link: https://usc.zoom.us/j/96796759326?pwd=aTF3SnJlS3ljM1pjMkhZNzIyVGttdz09

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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