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dc.contributor.authorTan, Rong Kun Jason
dc.contributor.supervisorVeeramani Shanmugamen_US

Cloud computing is identified to be a promising solution to performing big data analytics. However, the maximization of cloud utilization incorporated with optimizing intranode, internode, and memory management is still an open-ended challenge. This thesis presents a novel resource allocation model for cloud to load-balance data-agnostic tasks, minimizing intranode and internode delays, and decreasing memory consumption where these processes are involved in big data analytics. In conclusion, the proposed model outperforms existing techniques.

dc.publisherCurtin Universityen_US
dc.titleScalable Data-agnostic Processing Model with a Priori Scheduling for the Clouden_US
curtin.departmentElectrical Engineering, Computing and Mathematical Science (EECMS)en_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US

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