{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81779"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81779","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency","abstract":"The self-diagnosing capability of our service comes from the scalable learning-based performance problem diagnosis techniques we propose. The increasing complexity of systems has motivated design of machine learning approaches to automate some system management tasks. However, with increase in scale, current approaches suffer from serious scalability issues. We present two scalable learning-based techniques that automatically identify probable causes of performance problems in large server systems with multiple tiers and replicated sites. By incorporating a large number of diagnostic information sources using a temporal segmentation mechanism and applying transfer learning techniques, we achieve both scalability and improved diagnosis accuracy.","abstract_html":"The self-diagnosing capability of our service comes from the scalable learning-based performance problem diagnosis techniques we propose. The increasing complexity of systems has motivated design of machine learning approaches to automate some system management tasks. However, with increase in scale, current approaches suffer from serious scalability issues. We present two scalable learning-based techniques that automatically identify probable causes of performance problems in large server systems with multiple tiers and replicated sites. By incorporating a large number of diagnostic information sources using a temporal segmentation mechanism and applying transfer learning techniques, we achieve both scalability and improved diagnosis accuracy.","abstract_has_math":false,"creators":["Huang, Chengdu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:20:25Z","date_published":"2015-09-25T20:20:25Z","updated_at":"2026-07-22T22:26:16Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3290251"],"render_values":[{"text":"(MiAaPQ)AAI3290251","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81779","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek F."]},{"key":"dc:creator","label":"Author","values":["Huang, Chengdu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:20:25Z","10000-01-01","2007"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81779","(MiAaPQ)AAI3290251"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The self-diagnosing capability of our service comes from the scalable learning-based performance problem diagnosis techniques we propose. The increasing complexity of systems has motivated design of machine learning approaches to automate some system management tasks. However, with increase in scale, current approaches suffer from serious scalability issues. We present two scalable learning-based techniques that automatically identify probable causes of performance problems in large server systems with multiple tiers and replicated sites. By incorporating a large number of diagnostic information sources using a temporal segmentation mechanism and applying transfer learning techniques, we achieve both scalability and improved diagnosis accuracy.","Made available in DSpace on 2015-09-25T20:20:25Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3290251.pdf: 5234503 bytes, checksum: b6f3f5ffb9e4d768c66a08ff9a2834ea (MD5) Previous issue date: 2007","Embargo set by: Seth Robbins for item 83060 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","156 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007."]},{"key":"dc:title","label":"Title","values":["A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency"]}]}],"canonical_facts":{"dc:contributor":["Abdelzaher, Tarek F."],"dc:creator":["Huang, Chengdu"],"dc:date":["2015-09-25T20:20:25Z","10000-01-01","2007"],"dc:description":["The self-diagnosing capability of our service comes from the scalable learning-based performance problem diagnosis techniques we propose. The increasing complexity of systems has motivated design of machine learning approaches to automate some system management tasks. However, with increase in scale, current approaches suffer from serious scalability issues. We present two scalable learning-based techniques that automatically identify probable causes of performance problems in large server systems with multiple tiers and replicated sites. By incorporating a large number of diagnostic information sources using a temporal segmentation mechanism and applying transfer learning techniques, we achieve both scalability and improved diagnosis accuracy.","Made available in DSpace on 2015-09-25T20:20:25Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3290251.pdf: 5234503 bytes, checksum: b6f3f5ffb9e4d768c66a08ff9a2834ea (MD5) Previous issue date: 2007","Embargo set by: Seth Robbins for item 83060 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","156 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007."],"dc:identifier":["http://hdl.handle.net/2142/81779","(MiAaPQ)AAI3290251"],"dc:language":["eng"],"dc:subject":["Computer Science"],"dc:title":["A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:16Z"}