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A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

Tang, Maolin & Mohd Yusoh, Zeratul Izzah (2012) A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing. In Lecture Notes in Computer Science (LNCS), Springer Berlin Heidelberg, Villa Diodoro Hotel, Taormina, pp. 225-234.

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Abstract

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

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ID Code: 51613
Item Type: Conference Paper
Keywords: Cooperative Coevolution, Genetic Algoritgm, SaaS, Cloud Computing, Composite SaaS Placement
DOI: 10.1007/978-3-642-32964-7_23
ISBN: 978-364232963-0
ISSN: 0302-9743
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: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: © 2012 Springer-Verlag.
Deposited On: 16 Jul 2012 08:56
Last Modified: 06 Jan 2013 19:46

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