Performance optimization for distributed machine learning and graph processing at scale over virtualized infrastructure
Nowadays, many real-world applications can be represented as machine learning and graph processing (MLGP) problems, and require sophisticated analysis on massive datasets. Various distributed computing systems have been proposed to run MLGP applications in a cluster. These systems usually manage the...
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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/73229 |