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Scalable Automatic Service Composition using Genetic Algorithms


Scalable Automatic Service Composition using Genetic Algorithms

Roostaei Ali Mehr, Pouria (2023) Scalable Automatic Service Composition using Genetic Algorithms. Masters thesis, Concordia University.

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A composition of simple web services, each dedicated to performing a specific sub- task involved, proves to be a more competitive solution than an equivalent atomic web service for a complex requirement comprised of several sub-tasks. Composite services have been extensively researched and perfected in many aspects for over two decades, owing to benefits such as component re-usability, broader options for composition requesters, and the liberty to specialize for component providers. However, most studies in this field must acknowledge that each web service has a limited context in which it can successfully perform its tasks, the boundaries defined by the internal constraints imposed on the service by its providers. The restricted context-spaces of all such component services define the contextual boundaries of the composite service as a whole when used in a composition, making internal constraints an essential factor in composite service functionality. Due to their limited exposure, no systems have yet been proposed on the large-scale solution repository to cater to the specific verification of internal constraints imposed on components of a composite service. In this thesis, we propose a scalable automatic service composition capable of not only automatically constructing context-aware composite web services with internal constraints positioned for optimal resource utilization but also validating the generated compositions on a large-scale solution repository using the General Intensional Programming System (GIPSY) as a time- and cost-efficient simulation/execution environment.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Roostaei Ali Mehr, Pouria
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:3 January 2023
Thesis Supervisor(s):Paquet, Joey
ID Code:991736
Deposited By: Pouria Roostaei Ali Mehr
Deposited On:21 Jun 2023 14:42
Last Modified:21 Jun 2023 14:42
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