G. Kousalya

Bio: G. Kousalya is an academic researcher. The author has contributed to research in topics: The Internet & Problem statement. The author has an hindex of 1, co-authored 1 publications receiving 11 citations.

An Evolutionary Algorithmic Approach based Optimal Web Service Selection for Composition with Quality of Service

Abstract: Problem statement: Web service is a technology that provides flexibility and interconnection between different distributed applications over the Internet and intranets. When a client request cannot be satisfied by any individual service, existing web services can be combined into a composite web service. When there are a large number of Web services available, it is not easy to find an execution path of Web services composition that can satisfy the given request, since the search space for such a composition problem is in general exponentially increasing. Approach: In this study, we discuss and compare the two algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm for solving this optimization problem of optimal web service selection and composition. Results: The end results indicate PSO perform better over GA for single and multi user service selections. Conclusion: Inferences from the results indicate the service selections from the registry of pooled services can be optimized with the usage of Optimization Algorithms like GA and PSO.

QoS decomposition for service composition using genetic algorithm

Abstract: Recently, a lot of research has been dedicated to optimizing the QoS-aware service composition. This aims at selecting the optimal composed service from all possible service combinations regarding user's end-to-end quality requirements. Existing solutions often employ the global optimization approach, which does not show promising performance. Also, the complexity of such methods extensively depends on the number of available web-services, which continuously increase along with the growth of the Internet. Besides, the local optimization approaches have been rarely utilized, since they may violate the global constraints. In this paper, we propose a top-down structure, named quality constraints decomposition (QCD) here, to decompose the global constraints into the local constraints, using the genetic algorithm (GA). Then the best web service for each task is selected through a simple linear search. In contrast to existing methods, the QCD approach mainly depends on a limited set of tasks, which is considerably less complex, especially in the case of dynamically distributed service composition. Experimental results, based on a well-known data set of web services (QWSs), show the advantages of the QCD method in terms of computation time, considering the number of web services.

A Survey of Evolutionary Computation for Web Service Composition: A Technical Perspective

Abstract: Service oriented computing has emerged as a popular software development paradigm. In the era of Cloud computing, Big data, the Internet of Things (IoT) and Smart Cities, Web service composition has been extensively researched. Web service composition aims to find the best way of combining services, which accomplish simple tasks, into a more sophisticated composite application. Evolutionary computation lends itself to tackling the problem of Web service composition, since it allows for the optimisation of the overall Quality of Service attributes of the composite solution. In order to gain a better understanding of the different evolutionary computation-based approaches applied to this problem, a number of literature surveys have been written in this area. However, these surveys do not focus on the technical aspects of using evolutionary computation to this end, instead focusing on the general application of methods. Thus, the focus of this survey is on analysing existing works from a technical perspective, paying particular attention to the following key decisions when choosing an evolutionary computation-based approach for Web service composition: a) the representation of candidates, b) the fitness evaluation strategy, c) the handling of correctness constraints, d) the choice of evolutionary algorithms and operators. Based on these analyses, current trends, limitations, and future research paths are identified.

A quality of service aware multi-level strategy for selection of optimal web service

Abstract: The Utility-Infrastructure providers serve as an intermediary between the service providers and the service consumers The challenge of selecting optimal Web service by these service providers based on service consumer's preference using aggregate, score or degree has been tackled to a certain extent where optimal service selection is done in the context of subjective criterion alone One major challenge being faced by these providers is the selection of optimal Web service when there are ties with the used criterion where the performance alternatives have the same score We propose a Quality of Service aware multi-level strategy for selection of optimal Web services for Utility-Infrastructure providers To get optimal Web service selection, the service consumer's Quality of Service preference is compared with the Web service Quality of Service offerings The offering that best matches the Quality of Service preference is taken to be the optimal one We consider two alternative e-Market services for our Quality of Service driven service selection: the Information services and the Complex services We concentrate on the Information services and our model uses the non deterministic Quality of Service metrics In our experiment, we use Web service data set Quality of Service information as our input and the experimental results show that our proposed multilevel strategy model can satisfy service consumers' request based on our non-functional requirements

A study on evolutionary computing based web service selection techniques

Abstract: Many service providers are offering their business functionality as web services. The problem of web service selection is a complex and time-consuming activity. Among other techniques, a significant work has been reported on the use of evolutionary computing based algorithms in determining optimal web service for a task. A rigorous review of the state-of-the-art for efficient selection of web services using evolutionary computing based algorithms published over the last decade is presented. The existing works on web service selection using various evolutionary approaches with a discussion on algorithmic variations, their effect on selection, quality of service parameters used, contributions, limitations and research gaps of these works are explored.

Web service composition using PSO — ACO

Abstract: Web services are constructed to build the electronic business application to provide flexibility and interconnection of different applications using the internet. When a user can not satisfied any individual service then in order to satisfy the need of user request service, composition is performed to compose the available services. When there are different web services available then we need a suitable method to perform the composition of services depending upon the quality of service parameters. In our proposed work, we tried to compose the web service with maximum overall QoS valuesaccording to the user's queries. To solve the service composition, we applied quality of service (QoS) parameters, a combination of particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms for service composition.