Optimized online scheduling algorithms
© Springer International Publishing AG 2018. However, all algorithms mentioned in Chap. 2 are considered as concurrent processing but not parallel processing and all are suitable to handle non-data intensive applications in cloud environment as all are considered as complex algorithm which consumes...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/68808 |
| _version_ | 1848761895090126848 |
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| author | Tan, R. Leong, J. Sidhu, Amandeep |
| author_facet | Tan, R. Leong, J. Sidhu, Amandeep |
| author_sort | Tan, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © Springer International Publishing AG 2018. However, all algorithms mentioned in Chap. 2 are considered as concurrent processing but not parallel processing and all are suitable to handle non-data intensive applications in cloud environment as all are considered as complex algorithm which consumes relatively high amount of memory, bandwidth and computational power to maintain its data structure. The outcome of maintaining these data structures will cause the time of scheduling tasks unbounded and make loss in profit gains. Undeniably, profit gain by IaaS provider is inversely proportional to time consumed to finish a task. To encounter most of the aspects and issues which are mentioned in Chap. 3, this project propose an online scheduling algorithm is to overcome the various excessive overheads during process while maintaining service performance and comparable least time consuming approach for data intensive task to adapt in the future cloud system. |
| first_indexed | 2025-11-14T10:38:56Z |
| format | Book Chapter |
| id | curtin-20.500.11937-68808 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:38:56Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-688082018-06-29T12:35:37Z Optimized online scheduling algorithms Tan, R. Leong, J. Sidhu, Amandeep © Springer International Publishing AG 2018. However, all algorithms mentioned in Chap. 2 are considered as concurrent processing but not parallel processing and all are suitable to handle non-data intensive applications in cloud environment as all are considered as complex algorithm which consumes relatively high amount of memory, bandwidth and computational power to maintain its data structure. The outcome of maintaining these data structures will cause the time of scheduling tasks unbounded and make loss in profit gains. Undeniably, profit gain by IaaS provider is inversely proportional to time consumed to finish a task. To encounter most of the aspects and issues which are mentioned in Chap. 3, this project propose an online scheduling algorithm is to overcome the various excessive overheads during process while maintaining service performance and comparable least time consuming approach for data intensive task to adapt in the future cloud system. 2018 Book Chapter http://hdl.handle.net/20.500.11937/68808 10.1007/978-3-319-73214-5_5 restricted |
| spellingShingle | Tan, R. Leong, J. Sidhu, Amandeep Optimized online scheduling algorithms |
| title | Optimized online scheduling algorithms |
| title_full | Optimized online scheduling algorithms |
| title_fullStr | Optimized online scheduling algorithms |
| title_full_unstemmed | Optimized online scheduling algorithms |
| title_short | Optimized online scheduling algorithms |
| title_sort | optimized online scheduling algorithms |
| url | http://hdl.handle.net/20.500.11937/68808 |