Mohd Yusoh, Zeratul Izzah and Tong, Maolin (2010) A Penalty-Based Genetic Algorithm For The Composite Saas Placement Problem In The Cloud. In: 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 , 18-23 July 2010, Barcelona. (Submitted)
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A PENALTY-BASED GENETIC ALGORITHM FOR THE COMPOSITE SAAS PLACEMENT PROBLEM IN THE CLOUD-ZERATUL IZZAH MOHD YUSOH-MAK 00311 RAF.pdf Download (5MB) |
Abstract
Cloud computing is a latest new computing paradigm where applications, data and IT services, are provided over the Internet. Cloud computing has become a main medium for Software as a Service (SaaS) providers to host their SaaS as it can provide the scalability a SaaS requires. The challenges in the composite SaaS placement process rely on several factors including the large size of the Cloud network, SaaS competing resource requirements, SaaS interactions between its components and SaaS interactions with its data components. However, existing applications' placement methods in data centres are not concerned with the placement of the component's data. I n addition, a Cloud network is much larger than data center networks that have been discussed in existing studies. This paper proposes a penalty-based genetic algorithm (GA) to the composite SaaS placement problem in the Cloud. We believe this is the first attempt to the SaaS placement with its data in Cloud provider's sewers. Experimental results demonstrate the feasibility and the scalability of the GA.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Genetic algorithms, Cloud computing, Industrial engineering -- Mathematical models |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Information and Communication Technology |
| Depositing User: | Users 4097 not found. |
| Date Deposited: | 14 Dec 2017 08:11 |
| Last Modified: | 14 Dec 2017 08:11 |
| URI: | http://eprints.utem.edu.my/id/eprint/20152 |
| Statistic Details: | View Download Statistic |
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