{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/13386"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/13386","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient On-Demand Operations in Large-Scale Infrastructures","abstract":"In large-scale distributed infrastructures such as clouds, Grids, peer-to-peer systems, and wide-area testbeds, users and administrators typically desire to perform on-demand operations that deal with the most up-to-date state of the infrastructure. However, the scale and dynamism present in the operating environment make it challenging to support on-demand operations efficiently, i.e., in a bandwidth- and response-efficient manner. This dissertation discusses several on-demand operations, challenges associated with them, and system designs that meet these challenges. Specifically, we design and implement techniques for 1) on-demand group monitoring that allows users and administrators of an infrastructure to query and aggregate the up-to-date state of the machines (e.g., CPU utilization) in one or multiple groups, 2) on-demand storage for intermediate data generated by dataflow programming paradigms running in clouds, 3) on-demand Grid scheduling that makes worker-centric scheduling decisions based on the current availability of compute nodes, and 4) on-demand key/value pair lookup that is overlay-independent and perturbation-resistant. We evaluate these on-demand operations using large-scale simulations with traces gathered from real systems, as well as via deployments over real testbeds such as Emulab and PlanetLab.","abstract_html":"In large-scale distributed infrastructures such as clouds, Grids, peer-to-peer systems, and wide-area testbeds, users and administrators typically desire to perform on-demand operations that deal with the most up-to-date state of the infrastructure. However, the scale and dynamism present in the operating environment make it challenging to support on-demand operations efficiently, i.e., in a bandwidth- and response-efficient manner. This dissertation discusses several on-demand operations, challenges associated with them, and system designs that meet these challenges. Specifically, we design and implement techniques for 1) on-demand group monitoring that allows users and administrators of an infrastructure to query and aggregate the up-to-date state of the machines (e.g., CPU utilization) in one or multiple groups, 2) on-demand storage for intermediate data generated by dataflow programming paradigms running in clouds, 3) on-demand Grid scheduling that makes worker-centric scheduling decisions based on the current availability of compute nodes, and 4) on-demand key/value pair lookup that is overlay-independent and perturbation-resistant. We evaluate these on-demand operations using large-scale simulations with traces gathered from real systems, as well as via deployments over real testbeds such as Emulab and PlanetLab.","abstract_has_math":false,"creators":["Ko, Steven Y."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gupta, Indranil","Nahrstedt, Klara","Abdelzaher, Tarek F.","Milojicic, Dejan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-08-05T01:17:36Z","date_published":"2009-08-05T01:17:36Z","updated_at":"2026-07-22T22:25:04Z","subjects":["Distributed Systems"],"languages":["en"],"rights":["Copyright 2009 Steven Y. Ko"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/13386","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gupta, Indranil","Nahrstedt, Klara","Abdelzaher, Tarek F.","Milojicic, Dejan"]},{"key":"dc:creator","label":"Author","values":["Ko, Steven Y."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2009-08-05T01:17:36Z","2009-08-04"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation / Thesis","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":["Distributed Systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2009 Steven Y. 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Specifically, we design and implement techniques for 1) on-demand group monitoring that allows users and administrators of an infrastructure to query and aggregate the up-to-date state of the machines (e.g., CPU utilization) in one or multiple groups, 2) on-demand storage for intermediate data generated by dataflow programming paradigms running in clouds, 3) on-demand Grid scheduling that makes worker-centric scheduling decisions based on the current availability of compute nodes, and 4) on-demand key/value pair lookup that is overlay-independent and perturbation-resistant. We evaluate these on-demand operations using large-scale simulations with traces gathered from real systems, as well as via deployments over real testbeds such as Emulab and PlanetLab.","Submitted by Steven Ko (sko@illinois.edu) on 2009-08-05T01:17:36Z No. of bitstreams: 1 thesis.pdf: 1722965 bytes, checksum: 06915335fa094c83fff3c5ef9ffcf782 (MD5)","Made available in DSpace on 2009-08-05T01:17:36Z (GMT). No. of bitstreams: 1 thesis.pdf: 1722965 bytes, checksum: 06915335fa094c83fff3c5ef9ffcf782 (MD5) Previous issue date: 2009-08-04"]},{"key":"dc:title","label":"Title","values":["Efficient On-Demand Operations in Large-Scale Infrastructures"]}]}],"canonical_facts":{"dc:contributor":["Gupta, Indranil","Nahrstedt, Klara","Abdelzaher, Tarek F.","Milojicic, Dejan"],"dc:creator":["Ko, Steven Y."],"dc:date":["2009-08-05T01:17:36Z","2009-08-04"],"dc:description":["In large-scale distributed infrastructures such as clouds, Grids, peer-to-peer systems, and wide-area testbeds, users and administrators typically desire to perform on-demand operations that deal with the most up-to-date state of the infrastructure. However, the scale and dynamism present in the operating environment make it challenging to support on-demand operations efficiently, i.e., in a bandwidth- and response-efficient manner. This dissertation discusses several on-demand operations, challenges associated with them, and system designs that meet these challenges. Specifically, we design and implement techniques for 1) on-demand group monitoring that allows users and administrators of an infrastructure to query and aggregate the up-to-date state of the machines (e.g., CPU utilization) in one or multiple groups, 2) on-demand storage for intermediate data generated by dataflow programming paradigms running in clouds, 3) on-demand Grid scheduling that makes worker-centric scheduling decisions based on the current availability of compute nodes, and 4) on-demand key/value pair lookup that is overlay-independent and perturbation-resistant. We evaluate these on-demand operations using large-scale simulations with traces gathered from real systems, as well as via deployments over real testbeds such as Emulab and PlanetLab.","Submitted by Steven Ko (sko@illinois.edu) on 2009-08-05T01:17:36Z No. of bitstreams: 1 thesis.pdf: 1722965 bytes, checksum: 06915335fa094c83fff3c5ef9ffcf782 (MD5)","Made available in DSpace on 2009-08-05T01:17:36Z (GMT). No. of bitstreams: 1 thesis.pdf: 1722965 bytes, checksum: 06915335fa094c83fff3c5ef9ffcf782 (MD5) Previous issue date: 2009-08-04"],"dc:identifier":["http://hdl.handle.net/2142/13386"],"dc:language":["en"],"dc:rights":["Copyright 2009 Steven Y. 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