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dc.contributor.advisorRivera-Gallego, Wilson
dc.contributor.authorSanabria-Ordonez, John A.
dc.description.abstractThe goal of this thesis is to take a step to understanding the resource management of massively and scattered distributed systems. A framework to predict grid resources behavior and leverage the execution of long running tasks over computational grids has been developed. This framework employs statistical analysis for estimating the resource behavior and uses a divisible load approach to increase the throughput and reduce the idle time exhibited by computational resources. The proposed approach focuses on an opportunistic pull resource selection mechanism: a number of very light agents are deployed in nonintrusive way running in a user space. Initially the framework collects information on user requirements and application deployment, assigns a subset of jobs to available resources, and periodically the selected pool of resources is updated to opportunistically choose the resources that better complete the assigned jobs. The statistical analysis process evaluates in run time different probabilistic functions to determine the one that better model a resource behavior. Experimental results show a significant reduction of the application makespan along with good estimations of the resource behavior.en_US
dc.description.sponsorshipThis work was partially supported by the NSF CISE-CNS Grant No. 0424546 under the WALSAIP (Wide Area Large Scale Automated Information Processing) Project.en_US
dc.subjectResource orchestrationen_US
dc.subjectAdaptive orchestrationen_US
dc.titleAdaptive orchestration of resources in distributed wide area large scale infrastructuresen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2009 John Alexander Sanabria Ordonezen_US
dc.contributor.committeeSeguel, Jaime
dc.contributor.committeeRodriguez, Domingo
dc.contributor.committeeRodriguez, Manuel
dc.contributor.representativeResto, Pedro and Information Sciences and Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US

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    Items included under this collection are theses, dissertations, and project reports submitted as a requirement for completing a degree at UPR-Mayagüez.

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