{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/103571"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/103571","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Characterizing performance of residential internet connections using an analysis of measuring broadband America's web browsing test data","abstract":"This thesis presents an analysis of F.C.C.-measured web page loading times as observed in 2013 from nodes connected to consumer broadband providers in the Northeastern, Southern and Pacific U.S. We also collected data for multiple months in 2015 from the MIT network. We provide temporal and statistical analyses on total loading times for both datasets. We present four main contributions. First, we find differences in loading times for various websites that are consistent across providers and regions, showing the impact of infrastructure of transit and content providers on loading times and Quality of Experience (QoE.) Second, we find strong evidence of diurnal variation in loading times, highlighting the impact of network and server load on end-user QoE. Third, we show instances of localized congestion that severely impair the performance of some websites when measured from a residential provider. Fourth, we find that web loading times correlate with the size of a website's infrastructure as estimated by the number of IP addresses observed in the data. Finally, we also provide a set of policy recommendations: execution of javascript and other code during the web browsing test to more adequately capture loading times; expanding the list of target websites and collecting trace route data; collection of browsing data from non-residential networks; and public provision of funding for research on Measuring Broadband America's web browsing data. The websites studied in this thesis are: Amazon, CNN, EBay, Facebook, Google, msn, Wikipedia, Yahoo and YouTube.","abstract_html":"This thesis presents an analysis of F.C.C.-measured web page loading times as observed in 2013 from nodes connected to consumer broadband providers in the Northeastern, Southern and Pacific U.S. We also collected data for multiple months in 2015 from the MIT network. We provide temporal and statistical analyses on total loading times for both datasets. We present four main contributions. First, we find differences in loading times for various websites that are consistent across providers and regions, showing the impact of infrastructure of transit and content providers on loading times and Quality of Experience (QoE.) Second, we find strong evidence of diurnal variation in loading times, highlighting the impact of network and server load on end-user QoE. Third, we show instances of localized congestion that severely impair the performance of some websites when measured from a residential provider. Fourth, we find that web loading times correlate with the size of a website's infrastructure as estimated by the number of IP addresses observed in the data. Finally, we also provide a set of policy recommendations: execution of javascript and other code during the web browsing test to more adequately capture loading times; expanding the list of target websites and collecting trace route data; collection of browsing data from non-residential networks; and public provision of funding for research on Measuring Broadband America's web browsing data. The websites studied in this thesis are: Amazon, CNN, EBay, Facebook, Google, msn, Wikipedia, Yahoo and YouTube.","abstract_has_math":false,"creators":["Gamero-Garrido, Alexander M"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","school":null,"contributors":[],"advisors":["David D. Clark."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-22T22:21:50Z","subjects":["Institute for Data, Systems, and Society.","Engineering Systems Division.","Technology and Policy Program."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/103571","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["David D. Clark."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Massachusetts Institute of Technology. Engineering Systems Division.","Technology and Policy Program."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Technology and Policy Program."]},{"key":"dc:creator","label":"Author","values":["Gamero-Garrido, Alexander M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-07-11T14:44:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-07-11T14:44:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Institute for Data, Systems, and Society.","Engineering Systems Division.","Technology and Policy Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/103571"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Technology and Policy Program, 2015.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 71-73)."]},{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents an analysis of F.C.C.-measured web page loading times as observed in 2013 from nodes connected to consumer broadband providers in the Northeastern, Southern and Pacific U.S. We also collected data for multiple months in 2015 from the MIT network. We provide temporal and statistical analyses on total loading times for both datasets. We present four main contributions. First, we find differences in loading times for various websites that are consistent across providers and regions, showing the impact of infrastructure of transit and content providers on loading times and Quality of Experience (QoE.) Second, we find strong evidence of diurnal variation in loading times, highlighting the impact of network and server load on end-user QoE. Third, we show instances of localized congestion that severely impair the performance of some websites when measured from a residential provider. Fourth, we find that web loading times correlate with the size of a website's infrastructure as estimated by the number of IP addresses observed in the data. Finally, we also provide a set of policy recommendations: execution of javascript and other code during the web browsing test to more adequately capture loading times; expanding the list of target websites and collecting trace route data; collection of browsing data from non-residential networks; and public provision of funding for research on Measuring Broadband America's web browsing data. The websites studied in this thesis are: Amazon, CNN, EBay, Facebook, Google, msn, Wikipedia, Yahoo and YouTube."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Technology and Policy"]},{"key":"dc:title","label":"Title","values":["Characterizing performance of residential internet connections using an analysis of measuring broadband America's web browsing test data"]}]}],"canonical_facts":{"dc:contributor.advisor":["David D. Clark."],"dc:contributor.department":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Massachusetts Institute of Technology. Engineering Systems Division.","Technology and Policy Program."],"dc:contributor.other":["Technology and Policy Program."],"dc:creator":["Gamero-Garrido, Alexander M"],"dc:date.accessioned":["2016-07-11T14:44:16Z"],"dc:date.available":["2016-07-11T14:44:16Z"],"dc:date.issued":["2015"],"dc:description":["Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Technology and Policy Program, 2015.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 71-73)."],"dc:description.abstract":["This thesis presents an analysis of F.C.C.-measured web page loading times as observed in 2013 from nodes connected to consumer broadband providers in the Northeastern, Southern and Pacific U.S. We also collected data for multiple months in 2015 from the MIT network. We provide temporal and statistical analyses on total loading times for both datasets. We present four main contributions. First, we find differences in loading times for various websites that are consistent across providers and regions, showing the impact of infrastructure of transit and content providers on loading times and Quality of Experience (QoE.) Second, we find strong evidence of diurnal variation in loading times, highlighting the impact of network and server load on end-user QoE. Third, we show instances of localized congestion that severely impair the performance of some websites when measured from a residential provider. Fourth, we find that web loading times correlate with the size of a website's infrastructure as estimated by the number of IP addresses observed in the data. Finally, we also provide a set of policy recommendations: execution of javascript and other code during the web browsing test to more adequately capture loading times; expanding the list of target websites and collecting trace route data; collection of browsing data from non-residential networks; and public provision of funding for research on Measuring Broadband America's web browsing data. The websites studied in this thesis are: Amazon, CNN, EBay, Facebook, Google, msn, Wikipedia, Yahoo and YouTube."],"dc:description.degree":["S.M. in Technology and Policy"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/103571"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Institute for Data, Systems, and Society.","Engineering Systems Division.","Technology and Policy Program."],"dc:title":["Characterizing performance of residential internet connections using an analysis of measuring broadband America's web browsing test data"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:50Z"}