Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud
dc.contributor.author | Tan, Rong Kun Jason | |
dc.contributor.supervisor | Veeramani Shanmugam | en_US |
dc.date.accessioned | 2019-05-20T00:23:56Z | |
dc.date.available | 2019-05-20T00:23:56Z | |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/75449 | |
dc.description.abstract |
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. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | MPhil | en_US |
curtin.department | Electrical Engineering, Computing and Mathematical Science (EECMS) | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |