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Scalable Stochastic Models for Cloud Services

dc.contributor.advisor Trivedi, Kishor S.
dc.contributor.author Ghosh, Rahul
dc.date.accessioned 2013-01-16T20:26:53Z
dc.date.available 2013-01-16T20:26:53Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10161/6110
dc.description.abstract <p>Cloud computing appears to be a paradigm shift in service oriented computing. Massively scalable Cloud architectures are spawned by new business and social applications as well as Internet driven economics. Besides being inherently large scale and highly distributed, Cloud systems are almost always virtualized and operate in automated shared environments. The deployed Cloud services are still in their infancy and a variety of research challenges need to be addressed to predict their long-term behavior. Performance and dependability of Cloud services are in general stochastic in nature and they are affected by a large number of factors, e.g., nature of workload and faultload, infrastructure characteristics and management policies. As a result, developing scalable and predictive analytics for Cloud becomes difficult and non-trivial. This dissertation presents the research framework needed to develop high fidelity stochastic models for large scale enterprise systems using Cloud computing as an example. Throughout the dissertation, we show how the developed models are used for: (i) performance and availability analysis, (ii) understanding of power-performance trade-offs, (ii) resiliency quantification, (iv) cost analysis and capacity planning, and (v) risk analysis of Cloud services. In general, the models and approaches presented in this thesis can be useful to a Cloud service provider for planning, forecasting, bottleneck detection, what-if analysis or overall optimization during design, development, testing and operational phases of a Cloud.</p>
dc.subject Computer engineering
dc.subject Computer science
dc.subject Electrical engineering
dc.subject Analytics
dc.subject Cloud
dc.subject Markov chains
dc.subject Optimization
dc.subject Performance modeling
dc.subject Stochastic processes
dc.title Scalable Stochastic Models for Cloud Services
dc.type Dissertation
dc.department Electrical and Computer Engineering


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