Applications of Mixed Integer Programming to Cloud Computing: Modeling and Computation

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dc.contributor.advisorPerez-Salazar, Sebastianen_US
dc.contributor.advisorSchaefer, Andrew Jen_US
dc.creatorAlfant, Rachael Men_US
dc.date.accessioned2024-08-30T15:48:47Zen_US
dc.date.created2024-08en_US
dc.date.issued2024-05-10en_US
dc.date.submittedAugust 2024en_US
dc.date.updated2024-08-30T15:48:47Zen_US
dc.descriptionEMBARGO NOTE: This item is embargoed until 2026-08-01en_US
dc.description.abstractDemand for computing capacity in the cloud is generally not easily forecast; however, sub-optimal pricing and mis-allocation of cloud computing resources both have negative consequences for users and providers of cloud computing. This thesis approaches pricing and capacity allocation in cloud computing through the lens of stochastic mixed integer programming (SMIP), which provides a particularly useful framework for solving large, complex decision-making problems under uncertainty. Often, the uncertainty inherent to SMIPs manifests in the right-hand side (demand) vector. Thus, it is important to have a framework by which to assess a mixed integer programming (MIP) model’s quality over unknown or stochastic right-hand sides. As such, this thesis explores both theoretical and practical applications of SMIPs and MIPs with unknown right-hand sides. In particular, this thesis develops theoretical evaluative metrics for MIPs over multiple right-hand sides via gap functions, presents several stochastic optimization approaches to optimal pricing in the cloud, and formulates waste-minimizing (revenue-maximizing) SMIP models that optimize capacity allocation in the cloud.en_US
dc.embargo.lift2026-08-01en_US
dc.embargo.terms2026-08-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAlfant, Rachael M. Applications of Mixed Integer Programming to Cloud Computing: Modeling and Computation. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/117760en_US
dc.identifier.urihttps://hdl.handle.net/1911/117760en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectMixed Integer Programmingen_US
dc.subjectStochastic Programmingen_US
dc.subjectCloud Computingen_US
dc.titleApplications of Mixed Integer Programming to Cloud Computing: Modeling and Computationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputational and Applied Mathematicsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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