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dc.contributor.authorNaman, Omar
dc.date.accessioned2023-01-27 15:18:15 (GMT)
dc.date.available2024-01-28 05:50:04 (GMT)
dc.date.issued2023-01-27
dc.date.submitted2023-01-24
dc.identifier.urihttp://hdl.handle.net/10012/19136
dc.description.abstractI present MECBench, an extensible benchmarking framework for multi-access edge computing. MECBench is configurable and can emulate networks with different capabilities and conditions, can scale the generated workloads to mimic large number of clients, and can generate a range of workload patterns. MECBench is extensible; it can be extended to change the generated workload, use new datasets, and integrate new applications. MECBench’s implementation includes machine learning and synthetic edge applications. I demonstrate MECBench’s capabilities through three scenarios: an object detection processing for drone navigation, a natural language processing application, and a synthetic workload with configurable compute and I/O intensity. My evaluation shows that MECBench can be used to answer complex what-if questions pertaining to design and deployment decisions of MEC platforms and applications. My evaluation explores the impact of different combinations of applications, hardware, and network conditions as well as the cost-benefit tradeoff of different designs and configurations.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectbenchmarken
dc.subjectedge computingen
dc.subjectmulti-access edgeen
dc.titleMECBench: A Framework for Benchmarking Multi-Edge Computing Systemsen
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.terms1 yearen
uws.contributor.advisorAl-Kiswany, Samer
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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