With the increasing numbers of Web services and service users on World Wide Web, predicting QoS( Quality of Service ) for users will greatly aid service selection and discovery. Due to the different backgrounds and experiences of users, they have different QoS experiences when interacting with the same service. Even two users who have similar experiences on some services can have diverging views when considering other services. This paper proposes an approach to predict QoS. It is based on not only other users’ QoS experiences, but also the environment factor and user input factor. First bring forwards usage information feature model and calculate the similarity of two users based on the feature model. Then consider not only the historic information, but also environment and users’ inputs, such as bandwidth and data size. Before calculating the user similarity, select a set of Web services that have the highest degree of similarity with the target service, not all of the services. The missing value can be calculated through the data of similar services. The results of the experiment prove that our approach is feasible and effective.

A Web Service QoS Prediction Approach Based on Collaborative Filtering

Quality of service (QoS) is critical for service customers to select suitable web services for business applications in open web environment. However, since QoS are often influenced by several factors, traditional approaches are not very efficient and effective in measuring QoS of web service. The authors introduce in this study a novel QoS measure approach to efficiently measure QoS of web service for web service selection. The core of this approach is to take the three factors, that is, service providers, the context of customers and historical statistics into QoS measure. The experimental results demonstrate that the proposed approach is efficient and effective in measuring QoS of web service.

Quality of service measure approach of web service for service selection

With the ever increasing number of functional similar web services being made available on the Internet, how to leverage, aggregate and make use of individual component's quality of service(QoS) information to derive the optimal QoS of the composite service which meets the needs of users is still an ongoing hot research problem. In this paper, the current QoS models and related calculation methods are first introduced, and the current strategies and approaches of services selection are then systematically analyzed and discussed?DAt last, several drawbacks of existing algorithms are pointed out and some research directions of this field for the future are proposed.

A Survey on QoS-Aware Dynamic Web Service Selection

Trust degree and quality of service are nonfunctional properties of component service. In dynamic web service composition, trust degree is variable. It can be changed according to execution logs. In this paper we establish a novel model of dynamic web service composition based on variable trust degree and quality of service. Then we propose one adaptive ant colony optimization algorithm to solve this multi-objective optimization problem. In the final, a case study shows that the proposed algorithm can find more Pareto solution than the traditional ant colony optimization algorithm. And the results also prove that our novel algorithm has higher efficiency than the traditional one.

A Novel Adaptive Web Service Selection Algorithm Based on Ant Colony Optimization for Dynamic Web Service Composition

For the up-and-coming computing models like as cloud computing, service is the standard package for meeting all kinds of consumers' requirements. Web Services are the concrete implement of the service. When users request and consume Web Services, services' reputations will play a vital role in users' selection. A gradually adjusting reputation evaluation method of Web Services is proposed based on eliminating the collusive behaviors of consumers step by step, and a reputation-aware model for service selection is designed. In order to adjust reputations, QoS similarity is computed firstly according to the differences between advertised QoS from service providers and delivered QoS from service consumers' evaluation, next, current reputation is attained; then the consumers are sorted based on reputation using clustering algorithm and the potential collusive consumers are mined using association rules algorithm; finally, the updated reputation is recalculated and saved in the reputation center included in the model. The experimental results show that the model can identify the malicious consumers and improve the exact rate of reputation evaluation and success rate of service selection.

Reputation-aware Service Selection based on QoS Similarity

As the number of web services with the similar function is increasing, QoS-based service selection at runtime has become an important research topic. The existing QoS-based services selection approaches always assume that the QoS data coming from service providers and users are effective and trustworthy, which is actually impossible in reality. This paper proposes a service selection approach considering the trustworthiness of QoS data, which classifies and computes the QoS attributes according to the source of QoS data. For the QoS attributes whose data come from service providers, the statistics of past runtime data to revise the providers' QoS data are used. For the QoS attributes whose data come from users, feedback similarity to weigh users' QoS data is used. Furthermore, an implementation framework and a set of experiments are given, which show that this approach can effectively weaken the influence of untrustworthy QoS data on the services selection, thus can strengthen the accuracy of the

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Service Selection Approach Considering the Trustworthiness of QoS Data

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Service Selection Approach Considering the Trustworthiness of QoS Data