Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud
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...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/75449 |
| _version_ | 1848763484175597568 |
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| author | Tan, Rong Kun Jason |
| author_facet | Tan, Rong Kun Jason |
| author_sort | Tan, Rong Kun Jason |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:04:11Z |
| format | Thesis |
| id | curtin-20.500.11937-75449 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:04:11Z |
| publishDate | 2019 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-754492021-05-17T08:04:35Z Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud Tan, Rong Kun Jason 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. 2019 Thesis http://hdl.handle.net/20.500.11937/75449 Curtin University fulltext |
| spellingShingle | Tan, Rong Kun Jason Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title_full | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title_fullStr | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title_full_unstemmed | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title_short | Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud |
| title_sort | scalable data-agnostic processing model with a priori scheduling for the cloud |
| url | http://hdl.handle.net/20.500.11937/75449 |