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An Adaptive Design Pattern for Genetic Algorithm-Based Composition of Web Services in Autonomic Computing Systems Using SOA

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Advances in Grid and Pervasive Computing (GPC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7296))

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Abstract

Web services composition has been an active research area over the last few years. However, the technology is still not mature yet and several research issues need to be addressed. In this paper, we propose Genetic Algorithm based Design Pattern. This system provides tools for adaptive service composition and provisioning. We introduce a composition model where service context and exceptions are configurable to accommodate needs of different users. This allows for reusability of a service in different contexts and achieves a level of adaptive and contextualization without recoding and recompiling of the overall composed services. The proposed system will compose web services based on user request using Service oriented Architecture (SOA). Genetic Algorithm based composition Design Pattern satisfies properties of autonomic system. We use different Design Patterns for designing the system like, Master slave Design Pattern and Chain of responsibility Design Pattern. Our proposed system will satisfy all properties of autonomic system, for monitoring we have used context based monitoring, for decision making we use Master Slave which is based on decision making system that will reconfigure itself and Thread per connection is used of executing different services in different threads. A simple UML class and Sequence diagrams are depicted.

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© 2012 Springer-Verlag Berlin Heidelberg

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Mannava, V., Ramesh, T. (2012). An Adaptive Design Pattern for Genetic Algorithm-Based Composition of Web Services in Autonomic Computing Systems Using SOA. In: Li, R., Cao, J., Bourgeois, J. (eds) Advances in Grid and Pervasive Computing. GPC 2012. Lecture Notes in Computer Science, vol 7296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30767-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-30767-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30766-9

  • Online ISBN: 978-3-642-30767-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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