Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/69201
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dc.contributor.authorGong, Yifan-
dc.date.accessioned2016-11-24T09:20:40Z-
dc.date.available2016-11-24T09:20:40Z-
dc.date.issued2016-
dc.identifier.citationGong, Y. (2016). Performance and monetary cost optimizations for HPC applications in the Cloud. Doctoral thesis, Nanyang Technological University, Singapore.-
dc.identifier.urihttp://hdl.handle.net/10356/69201-
dc.description111 p.en_US
dc.description.abstractBenefit from cloud computing, high performance computing (HPC) tasks can be performed on virtual machines instead of a physical cluster. Because of the pay-as-you-go nature, performance and monetary cost optimizations are significant in not only improving productivity but also reducing ownership cost. This thesis focuses on both of them. For performance, virtualization hides network topology information and causes traffic interference. Many existing network optimizations that rely on topology information or stable network performance are no longer effective. So we develop novel performance optimization algorithms and further propose a novel network performance model to decouple the constant and volatility components from the dynamic network performance. For monetary cost, Amazon EC2 spot instances give us a chance to reduce costs for HPC applications. So we leverage both on-demand and spot instances to guarantee deadline constraint and develop a cost model guided approach to optimize the monetary cost.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titlePerformance and monetary cost optimizations for HPC applications in the clouden_US
dc.typeThesis-
dc.contributor.supervisorTang Xueyan-
dc.contributor.supervisorQin Xiaosheng-
dc.contributor.schoolInterdisciplinary Graduate School (IGS)-
dc.description.degreeDoctor of Philosophy (IGS)-
dc.identifier.doi10.32657/10356/69201-
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item.grantfulltextopen-
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