Biologically-inspired resource management for cloud computing
Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but res...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/42384 |
| _version_ | 1826111035960983552 |
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| author | Ching, Mark Chuen Teck. |
| author2 | Dai Changchun |
| author_facet | Dai Changchun Ching, Mark Chuen Teck. |
| author_sort | Ching, Mark Chuen Teck. |
| collection | NTU |
| description | Cloud computing allows the users to efficiently and dynamically provision
computing resources to meet their IT needs. Most cloud providers offer two types of
payment plans to the user, i.e., reservation and on-demand. The reservation plan is
typically cheaper than the on-demand plan but reservation plan has to be provisioned in
advance. Reserving the resources would be straightforward if the actual computing
demand (e.g., job processing) is known in advance. However, in reality, the actual
computing demand is known only at the point of actual usage. This makes it difficult to
reserve the correct amount of resources during the reservation to meet the computing
demands of the users. In this project, we propose an evolutionary optimal virtual
machine placement (EOVMP) algorithm with a demand forecaster. First, a demand
forecaster predicts the computing demand from user’s data. Then, EOVMP uses this
predicted demand to allocate the virtual machine using reservation and on-demand
plans for job processing. The performance of the proposed schemes is evaluated by
simulations and numerical studies. The evaluation result shows that the EOVMP
algorithm can provide the solution close to the optimal solution of stochastic integer
programming (SIP) and the prediction of the demand forecaster is of reasonable
accuracy. |
| first_indexed | 2024-10-01T02:44:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/42384 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:44:21Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/423842023-03-03T20:31:18Z Biologically-inspired resource management for cloud computing Ching, Mark Chuen Teck. Dai Changchun School of Computer Engineering Dusit Niyato DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand is known only at the point of actual usage. This makes it difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this project, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand from user’s data. Then, EOVMP uses this predicted demand to allocate the virtual machine using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy. Bachelor of Engineering (Computer Science) 2010-11-30T01:57:39Z 2010-11-30T01:57:39Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/42384 en Nanyang Technological University 66 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Ching, Mark Chuen Teck. Biologically-inspired resource management for cloud computing |
| title | Biologically-inspired resource management for cloud computing |
| title_full | Biologically-inspired resource management for cloud computing |
| title_fullStr | Biologically-inspired resource management for cloud computing |
| title_full_unstemmed | Biologically-inspired resource management for cloud computing |
| title_short | Biologically-inspired resource management for cloud computing |
| title_sort | biologically inspired resource management for cloud computing |
| topic | DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks |
| url | http://hdl.handle.net/10356/42384 |
| work_keys_str_mv | AT chingmarkchuenteck biologicallyinspiredresourcemanagementforcloudcomputing |