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.
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ABSTRACT: Nowadays web services are a distributed technology that can successfully solve integration problems between heterogeneous systems. The supporting technology for web services has been widely studied mainly focusing on the standardization of service transactions and running a single and multiple web services. In dealing with complex and large-scale web service requests, there is a foreseeable bottleneck of supporting technology. QoS-Aware web service composition is NP-hard problem and one of the most challenging problems in web services. This paper proposes an optimization solution using enhanced evolutionary algorithm (EA) for the dynamic composition of web service components and performs the trade-off analysis on dynamic composition of multi domain with multi objectives. First, we propose the Distance Function Based Evolutionary Algorithm (DFBEA) techniques to automatically select optimal combinations of web service components from available component repositories. Second, using multi domain with multi objectives case study, we demonstrate the trade-off analysis on efficiency and effectiveness of the proposed technique and algorithm through experimental evaluation of component selection, and furthermore a comparison with other optimization techniques will be proposed.International Journal of Advances in Information Sciences and Service Sciences. 06/2012; 4(6):9-25.
Conference Paper: A greedy approach for service composition[Show abstract] [Hide abstract]
ABSTRACT: Nowadays, service oriented architecture provides a scalable framework for service composition. Today's systems are tending to be large scaled, such as cloud workflows; in such systems, service composition algorithms play a critical role in composing multi-provider services by considering user desirable quality of services so as to fulfill business workflows. In this paper, we propose a greedy approach for mapping workflows to composed services considering all flow structures, such as joint, fork, loop and sequence, which are specified in workflow templates with various QoS parameters. We compare the presented approach with an ant colony optimization approach to show its performance and validity.Telecommunications (IST), 2012 Sixth International Symposium on; 01/2012
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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;