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<div class="">This is an announcement of Ruidan Li's MS Presentation</div>
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<div class="">Candidate: Ruidan Li</div>
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<div class="">Date: Thursday, August 17, 2023</div>
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<div class="">Time: 10 am CST</div>
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<div class="">Location: JCL 298</div>
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<div class="">Remote Location: <a href="https://uchicago.zoom.us/j/98464427616?pwd=b0I2ZENuemN0UEFGcnNpVUNzMXVXZz09&from=addon" id="LPlnkOWALinkPreview" class="">https://uchicago.zoom.us/j/98464427616?pwd=b0I2ZENuemN0UEFGcnNpVUNzMXVXZz09&from=addon</a></div>
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<div class="">M.S. Paper Title: FREP: Fleet Performance Evaluation Pipeline for Identifying Server Degradation in Large-Scale Datacenters</div>
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<div class="">Abstract: Stable and predictable performance of fleet servers is critical for providing reliable services. To identify and remove underperforming servers, performance data must be recorded and then systematically analyzed at scale. In this thesis,
we report on our methodology for collecting and analyzing performance data at CompanyX for the purposes of identifying and removing underperforming datacenter servers. We present (and will release), to the best of our knowledge, the largest dataset on CPU
and memory benchmarks. We show how off-the-shelf statistical techniques must be customized for the nature of our dataset, resulting in a custom pipeline, FREP, that leverages a wide range of statistical techniques. FREP comprises a variety of modules that
detect variances, divergences, and outliers, and has provided various types of important recommendations to CompanyX operators and helped improve their understanding of their fleet behaviors.</div>
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<div class="">Advisors: Haryadi Gunawi</div>
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<div class="">Committee Members: Haryadi Gunawi, Hank Hoffmann, and <span class="" style="letter-spacing:0.20000000298023224px; white-space:pre-wrap">Marty Humphrey</span></div>
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