Autonomic Performance Optimization with Application to Self-Architecting Software Systems

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dc.contributor.advisorMenasce, Daniel A.
dc.contributor.authorEwing, John Martin
dc.creatorEwing, John Martin
dc.date.accessioned2015-07-29T18:42:50Z
dc.date.available2015-07-29T18:42:50Z
dc.date.issued2015
dc.description.abstractService Oriented Architectures (SOA) are an emerging software engineering discipline that builds software systems and applications by connecting and integrating well-defined, distributed, reusable software service instances. SOA can speed development time and reduce costs by encouraging reuse, but this new service paradigm presents significant challenges. Many SOA applications are dependent upon service instances maintained by vendors and/or separate organizations. Applications and composed services using disparate providers typically demonstrate limited autonomy with contemporary SOA approaches. Availability may also suffer with the proliferation of possible points of failure--restoration of functionality often depends upon intervention by human administrators.
dc.format.extent207 pages
dc.identifier.urihttps://hdl.handle.net/1920/9702
dc.language.isoen
dc.rightsCopyright 2015 John Martin Ewing
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectAutonomic computing
dc.subjectMachine learning
dc.subjectMetaheuristics
dc.subjectOptimization
dc.subjectService-oriented architectures (SOA)
dc.subjectSupport vector machines (SVM)
dc.titleAutonomic Performance Optimization with Application to Self-Architecting Software Systems
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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