{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/10487"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/10487","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Evaluation of Internal Delay Inference in Queuing Networks","abstract":"Statistical inference of internal computer network characteristics using only externally made measurements is extremely useful in the analysis of highly complex networks. This the- sis seeks to implement and test an expectation-maximization (EM) algorithm that uses these observations to estimate total end-to-end network delay density, link delay density and prob- ability of route selection. The EM algorithm in question was tested using source/destination delays generated from a custom queuing network simulator. The parameters of the queuing network were varied in order to determine the e ectiveness of the algorithm on Jackson-type networks as well as more realistic networks. The subsequent results of the algorithm are compared against the actual network simulation data to evaluate the performance of the algorithm.","abstract_html":"Statistical inference of internal computer network characteristics using only externally made measurements is extremely useful in the analysis of highly complex networks. This the- sis seeks to implement and test an expectation-maximization (EM) algorithm that uses these observations to estimate total end-to-end network delay density, link delay density and prob- ability of route selection. The EM algorithm in question was tested using source/destination delays generated from a custom queuing network simulator. The parameters of the queuing network were varied in order to determine the e ectiveness of the algorithm on Jackson-type networks as well as more realistic networks. The subsequent results of the algorithm are compared against the actual network simulation data to evaluate the performance of the algorithm.","abstract_has_math":false,"creators":["Stoner, David E"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-27T19:52:00Z","subjects":["Bivariate Markov chain","EM algorithm","Queuing network","Python"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/10487"],"render_values":[{"text":"hdl:1920/10487","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bivariate Markov chain","EM algorithm","Queuing network","Python"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/10487"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Statistical inference of internal computer network characteristics using only externally made measurements is extremely useful in the analysis of highly complex networks. This the- sis seeks to implement and test an expectation-maximization (EM) algorithm that uses these observations to estimate total end-to-end network delay density, link delay density and prob- ability of route selection. The EM algorithm in question was tested using source/destination delays generated from a custom queuing network simulator. The parameters of the queuing network were varied in order to determine the e ectiveness of the algorithm on Jackson-type networks as well as more realistic networks. The subsequent results of the algorithm are compared against the actual network simulation data to evaluate the performance of the algorithm."]},{"key":"dc:title","label":"Title","values":["Evaluation of Internal Delay Inference in Queuing Networks"]}]}],"canonical_facts":{"dc:description.other":["Statistical inference of internal computer network characteristics using only externally made measurements is extremely useful in the analysis of highly complex networks. This the- sis seeks to implement and test an expectation-maximization (EM) algorithm that uses these observations to estimate total end-to-end network delay density, link delay density and prob- ability of route selection. The EM algorithm in question was tested using source/destination delays generated from a custom queuing network simulator. The parameters of the queuing network were varied in order to determine the e ectiveness of the algorithm on Jackson-type networks as well as more realistic networks. The subsequent results of the algorithm are compared against the actual network simulation data to evaluate the performance of the algorithm."],"dc:identifier":["hdl:1920/10487"],"dc:subject":["Bivariate Markov chain","EM algorithm","Queuing network","Python"],"dc:title":["Evaluation of Internal Delay Inference in Queuing Networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T19:52:00Z"}