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<div class="">This is an announcement of Martin Putra's MS Presentation</div>
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<div class="">Candidate: Martin Putra</div>
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<div class="">Date: Thursday, November 03, 2022</div>
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<div class="">Time: 11 am CST</div>
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<div class="">Remote Location: <a href="https://uchicago.zoom.us/j/8080588315?pwd=bHJHMWxaVS8rbFI5WUczdVlhMkhGUT09" class="">https://uchicago.zoom.us/j/8080588315?pwd=bHJHMWxaVS8rbFI5WUczdVlhMkhGUT09</a> </div>
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<div class="">Location: JCL 298</div>
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<div class="">M.S. Paper Title: HYBRID-CLOUD SCHEDULING FOR LONG-RUNNING BIOINFORMATICS WORKFLOWS UNDER TIME CONSTRAINTS</div>
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<div class="">Abstract: The amount of genomics data is increasing at a rapid pace. This leads to a situation where high-throughput data processing is needed to efficiently mine insights from the increasingly abundant genomics data. However, bioinformatics workflows/jobs
typically take large input files (e.g., hundreds of GB), resulting in jobs having long execution times.</div>
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<div class="">This thesis work seeks to answer the following research question: "How to ensure X% deadline satisfaction when scheduling bioinformatics workflows in hybrid clouds?". To address the problem, this thesis presents a novel hybrid cloud scheduler
that significantly reduces deadline miss rate at no or minimal extra cost. The new scheduler consists of two main components: 1) an accurate execution time predictor for bioinformatics workflows and 2) a novel scheduling policy for ensuring a high statistical
guarantee of completing job executions on spot instances leveraging spot lifetime distributions and job duration prediction. </div>
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<div class="">In this thesis, I developed a high-fidelity simulator that accurately resembles the behaviors of bioinformatics workflows on hybrid cloud environments composed of on-premise and cloud machines. The proposed scheduler was modeled on top of the
high-fidelity simulator and evaluated using large-scale, real-world production traces from one of the leading genomics research centers. The evaluation results show that the new scheduler successfully reduced deadline miss rates and/or cost compared to several
baseline scheduling policies. Finally, the thesis outlined future research directions for refining and implementing the scheduler on production-level genomics processing systems.</div>
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<div class="">Advisors: Robert Grossman and Haryadi Gunawi</div>
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<div class="">Committee Members: Robert Grossman, Haryadi Gunawi, and In Kee Kim</div>
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