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A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud

Yusoh, Zeratul & Tang, Maolin (2010) A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud. In Tang, Maolin (Ed.) Proceeding of the 2010 IEEE World Congress on Computational Intellgence, IEEE, Centre de Convencions Internacional de Barcelona, Barcelona, pp. 600-607.

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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. In 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 servers. Experimental results demonstrate the feasibility and the scalability of the GA.

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ID Code: 33292
Item Type: Conference Paper
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 IEEE
Deposited On: 29 Jul 2010 08:17
Last Modified: 01 Mar 2012 00:27

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