{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101212"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101212","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mélange: Multi-tenant scheduling with adaptive eviction for graph processing clusters","abstract":"Multi-tenancy is an important approach to resource consolidation in cluster management. In this thesis we design and evaluate Mélange, an efficient multi-tenant scheduler targeted towards graph processing jobs. Mélange supports job priorities and eviction, while attempting to avoid starvation. We propose novel ways of exploiting domain-specific knowledge to achieve better scheduling decisions for graph processing jobs. We evaluate static eviction policies and design Mélange to adapt to the cluster and job state at run time to reduce overhead costs during eviction. We have developed Mélange as a cross-layer scheduler built over Apache Giraph and YARN, and show experimental results with synthetic as well as production workloads.","abstract_html":"Multi-tenancy is an important approach to resource consolidation in cluster management. In this thesis we design and evaluate Mélange, an efficient multi-tenant scheduler targeted towards graph processing jobs. Mélange supports job priorities and eviction, while attempting to avoid starvation. We propose novel ways of exploiting domain-specific knowledge to achieve better scheduling decisions for graph processing jobs. We evaluate static eviction policies and design Mélange to adapt to the cluster and job state at run time to reduce overhead costs during eviction. We have developed Mélange as a cross-layer scheduler built over Apache Giraph and YARN, and show experimental results with synthetic as well as production workloads.","abstract_has_math":false,"creators":["Mehar, Jayasi"],"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":2018,"date_issued":"2018-05","date_published":"2018-05","updated_at":"2026-07-22T22:24:38Z","subjects":["graph processing","multi-tenancy","eviction","scheduling"],"languages":["en"],"rights":["Copyright 2018 Jayasi Mehar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101212","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":["Mehar, Jayasi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-05","2018-09-04T20:36:52Z","2020-09-05T09:15:09Z","2018-04-24"]},{"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":["graph processing","multi-tenancy","eviction","scheduling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Jayasi Mehar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101212"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Multi-tenancy is an important approach to resource consolidation in cluster management. 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In this thesis we design and evaluate Mélange, an efficient multi-tenant scheduler targeted towards graph processing jobs. Mélange supports job priorities and eviction, while attempting to avoid starvation. We propose novel ways of exploiting domain-specific knowledge to achieve better scheduling decisions for graph processing jobs. We evaluate static eviction policies and design Mélange to adapt to the cluster and job state at run time to reduce overhead costs during eviction. 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