QoS-Based Web Service Composition Accommodating Inter-service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm.
ABSTRACT In the field of semantic grid, QoS-based Web service composition is an important problem. In semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the composition consider not only QoS properties of Web services, but also inter service dependencies and conflicts which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address the Web service composition optimization problem in the presence of domain constraints and inter service dependencies and conflicts. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.
- SourceAvailable from: Mohammad Shojafar
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ABSTRACT: 2014 © The author(s) 2013. Published with open access at www.questjournals.org ABSTRACT:-Nowadays, some special attributes of web services such as being independent from platform, reusability, having a loosely coupled architecture and the ability to compose together in contrast to other applications have made them as components enjoying the capability of supporting various changes. Therefore, most organizations tend to use these application components in order to provide their customers with better services. Furthermore, the use of a single service cannot meet the needs of most customers. Consequently, the issue of composing these services aimed at increasing their efficiency is one of the important issues. As the number of these services is increasing day to day, the process of selecting and composition appropriate web services in terms of their quality and users' need from among numerous single services having same functionality but different quality attributes such as cost, response time, reliability and availability has become one of the main challenges associated with composing these web services. To solve this problem, much works have been done so far in various ways. In recent years, most of these studies have used a variety of heuristic algorithms or a combination of them. The reason for this is the acceptable running time of these algorithms to find a solution close to the optimal solution. Accordingly, the proposed methodology for this research is based on particle swarm heuristic algorithm inspired by patterns of birds. The main reasons to choose this algorithm include its flexibility, less parameters, easy to implement and low cost. In contrast, the disadvantage of this algorithm is premature convergence that has been tried to be solved by adding two functions of Inertia Coefficient Adjustment and Particle Modification to main algorithm. Following the N iterations of the algorithm, the inertia coefficient adjustment function is called. Modifying the amount of inertia coefficient, the function will be able to control how the search is performed and to decrease the running time. As the running process continues, the second function used to modify the particles aimed at improving them is called following the M iterations of the algorithm. It tries to prevent algorithm being trapped in a local optimum. Applying this function, some web services on top particles which do not satisfy the desired quality features of users will be replaced by a number of services on the dataset. This is done on condition that the services on the dataset are better than services on the particle. If the algorithm gets trapped in a local optimum, this function makes the algorithm to perform the search in a new sample space. The efficiency of this method is evaluated in a simulated environment and is compared with both conventional birds and genetic algorithms. The results indicate that the proposed method is more effective than conventional algorithms in terms of running time and success rate as well as addressing the problem of being trapped in a local optimum.01/2014;
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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.Applied Soft Computing. 07/2013; 13(7):3409–3421.