{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Perfect Sampling for Queueing Systems","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree"},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.format.mimetype","value":"Application/PDF"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/78367"},{"label":"dc.language.iso","value":"en_US"},{"label":"dcterms.abstract","value":"This thesis mainly studies perfect simulation methodology for the time-varying queueing systems including time varying queue with a single server ($M_t/G_t/1$ and G_t/G/1) and time-varying queue with multi-servers ($G_t/G/c$). Firstly, we introduce how to simulate the $M_t/G_t/1$ queue by an auxiliary process and then proposed a perfect sampling algorithm for it, which works effectively verified by numerical results. Secondly, two perfect sampling algorithms for the $G_t/G/1$ queue are proposed. One of them is based on a technique called Dominated Coupling From The Past (DCFTP), which was given by proposed by Propp and Wilson in 1996, by taking the Vacation System (VS) as a dominant queue. The other one shows how can $G_t/G/1$ be sampled directly without any dominant queues. Besides, corresponding numerical results are reported in the end to show the effectiveness of the algorithms. At last, we give two perfect sampling algorithms for $G_t/G/c$ queue. Both of them takes advantage of the DCFTP technique but with different dominant queues, which are the VS model and $G_t/G/c$ under Random Assignment (RA) discipline. The accuracy and efficiency of the algorithms are verified by corresponding numerical experiments. Moreover, the performances for VS and RA models are compared in the end, which shows that for large $c (=10)$, RA model performs better than VS."},{"label":"dcterms.available","value":"2018-07-09T14:28:03Z"},{"label":"dcterms.contributor","value":"Sasansuma, Katsunobu."},{"label":"dcterms.creator","value":"Shi, Xianjun"},{"label":"dcterms.dateAccepted","value":"2018-07-09T14:28:03Z"},{"label":"dcterms.dateSubmitted","value":"2018-07-09T14:28:03Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"105 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"SHI_grad.sunysb_0771E_13347.pdf"},{"label":"dcterms.issued","value":"2017-08-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Submitted by Jason Torre (fjason.torre@stonybrook.edu) on 2018-07-09T14:28:03Z\nNo. of bitstreams: 1\nSHI_grad.sunysb_0771E_13347.pdf: 2442796 bytes, checksum: 16e5f4d9989e3f3dec522db4deabf203 (MD5)"},{"label":"dcterms.subject","value":"Time-Varying"},{"label":"dcterms.title","value":"Perfect Sampling for Queueing Systems"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/37%2F63%2F80%2F37638076068080542243460642195604435953/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/37%2F63%2F80%2F37638076068080542243460642195604435953","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}