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dc.contributor.authorUdayashankar, Sreeharsha
dc.date.accessioned2021-04-30 15:20:44 (GMT)
dc.date.issued2021-04-30
dc.date.submitted2021-04-27
dc.identifier.urihttp://hdl.handle.net/10012/16922
dc.description.abstractThis thesis presents Bolt, a novel scheduler design for large-scale real-time data analytics. Bolt achieves the scheduling accuracy of modern centralized schedulers while supporting clusters with hundreds of thousands of nodes. At Bolt’s core is a scheduler design that leverages modern programmable switches. Bolt supports a FIFO scheduling policy, as well as task priority-based and task resource constraint-based scheduling policies. Evaluation of a Bolt prototype on our cluster with a Barefoot Tofino switch shows that the proposed approach can reduce scheduling overhead by 40x and increase the scheduling throughput by 50x compared to state-of-the-art centralized and decentralized schedulers.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectcloud computingen
dc.subjectnetworkingen
dc.subjectin-networken
dc.subjectschedulingen
dc.subjectlow latencyen
dc.titleIn-Network Scheduling for Real-Time Analyticsen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws-etd.embargo.terms2 yearsen
uws.contributor.advisorAl-Kiswany, Samer
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws-etd.embargo2023-04-30T15:20:44Z
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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