QoS-aware Service Selection Problem Research Articles

A comprehensive multi-objective approach of service selection for service processes with twofold restrictions

To build complex business processes with QoS constraints by services with inter-service correlations is a challenge. Meeting this challenge involves the QoS-aware service selection problem for service processes with twofold restrictions (QoS constraints and inter-service correlations). Recently, QoS-aware service selection has been addressed from a multi-objective perspective. However, existing research fails to comprehensively consider both QoS constraints and inter-service correlations that widely exist in a real business world. Besides, none of them comprehensively considers both service selection at build time and run time, which may lead to high economic compensation caused by breach penalties at run time. In this paper, we present a comprehensive multi-objective optimization model for service selection with twofold restrictions. Furthermore, an improved multi-objective evolutionary algorithm framework for this model (MOEAQI) is proposed, which can provide a systematic solution for service selection at build time and run time respectively. In this framework, a constrained-domination principle is designed for the specific objectives and constraints in different phases, and several improved strategies are proposed to increase the searching effectiveness and efficiency. Finally, three sets of experiments with different problem sizes are constructed, and our approach is tested in comparison to the state-of-the-art algorithms for service selection problems including GDE3, E3MOGA and DASC. The performance of each algorithm is evaluated in the aspects of set coverage, spacing and computation time. Experimental results demonstrate that our approach is more effective than existing methods for service selection with twofold restrictions.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

Discovering admissible Web services with uncertain QoS

Open and dynamic environments lead to inherent uncertainty of Web service QoS (Quality of Service), and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empirical distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services including alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance (SD) rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported progressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be improved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scalability of the proposed algorithm.

QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.

QoS-Aware Service Selection Algorithms for Pervasive Service Composition in Mobile Wireless Environments

The successful application of pervasive services running in mobile wireless networks and devices relies on its ability to provide efficient and cost-effective QoS (Quality of Service) support. This paper proposes a comprehensive QoS model specifically for pervasive services. It considers not only user-perceived factors but also mobile wireless network characteristics. The corresponding formula to calculate each QoS criterion is also devised. In particular, this paper formulates the QoS-aware service selection problem for pervasive service composition and proposes some solutions to the problem, i.e., global-search-based LOSSA (local optimal service selection algorithm) and limited broadcast based LOSSA-k. The evaluation results of the algorithms have shown the effectiveness of the QoS model and the efficiency of the proposed LOSSAs.