{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97724"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97724","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An experimental comparison of partitioning strategies in distributed graph processing","abstract":"In this thesis, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in two common systems (PowerLyra and GraphX).","abstract_html":"In this thesis, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in two common systems (PowerLyra and GraphX).","abstract_has_math":false,"creators":["Verma, Shiv"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gupta, Indranil"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T20:33:03Z","date_published":"2017-08-10T20:33:03Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Distributed","Graph","Processing","Partitioning"],"languages":["en"],"rights":["Copyright 2017 Shiv Verma"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97724","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gupta, Indranil"]},{"key":"dc:creator","label":"Author","values":["Verma, Shiv"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T20:33:03Z","2019-08-11T09:15:39Z","2017-04-24","2017-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Distributed","Graph","Processing","Partitioning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Shiv Verma"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97724"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in two common systems (PowerLyra and GraphX).","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-05-01","The student, Shiv Verma, accepted the attached license on 2017-04-17 at 19:28.","The student, Shiv Verma, submitted this Thesis for approval on 2017-04-17 at 19:40.","This Thesis was approved for publication on 2017-04-24 at 09:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10830 on 2017-08-10 at 15:05:59","Made available in DSpace on 2017-08-10T20:33:03Z (GMT). No. of bitstreams: 2 VERMA-THESIS-2017.pdf: 1176883 bytes, checksum: e49f2de22c65fd67d96121626f710849 (MD5) LICENSE.txt: 4207 bytes, checksum: eb422c7d45cb7c49bb2212e387d9fcaf (MD5) Previous issue date: 2017-04-24","Embargo set by: Colleen Fallaw for item 102777 Lift date: 2019-08-10T21:27:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 102777 on 2019-08-11T09:15:39Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An experimental comparison of partitioning strategies in distributed graph processing"]}]}],"canonical_facts":{"dc:contributor":["Gupta, Indranil"],"dc:creator":["Verma, Shiv"],"dc:date":["2017-08-10T20:33:03Z","2019-08-11T09:15:39Z","2017-04-24","2017-05"],"dc:description":["In this thesis, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in two common systems (PowerLyra and GraphX).","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-05-01","The student, Shiv Verma, accepted the attached license on 2017-04-17 at 19:28.","The student, Shiv Verma, submitted this Thesis for approval on 2017-04-17 at 19:40.","This Thesis was approved for publication on 2017-04-24 at 09:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10830 on 2017-08-10 at 15:05:59","Made available in DSpace on 2017-08-10T20:33:03Z (GMT). No. of bitstreams: 2 VERMA-THESIS-2017.pdf: 1176883 bytes, checksum: e49f2de22c65fd67d96121626f710849 (MD5) LICENSE.txt: 4207 bytes, checksum: eb422c7d45cb7c49bb2212e387d9fcaf (MD5) Previous issue date: 2017-04-24","Embargo set by: Colleen Fallaw for item 102777 Lift date: 2019-08-10T21:27:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 102777 on 2019-08-11T09:15:39Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/97724"],"dc:language":["en"],"dc:rights":["Copyright 2017 Shiv Verma"],"dc:subject":["Distributed","Graph","Processing","Partitioning"],"dc:title":["An experimental comparison of partitioning strategies in distributed graph processing"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:34Z"}