{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81860"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81860","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":"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":"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. 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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.","Made available in DSpace on 2015-09-25T20:20:43Z (GMT). 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