Efficient Approach Towards an Agent-Based Dynamic Web Service Discovery Framework with QoS Support

Abstract. Web services are about the integration of applications via the Web. Hereby, the programming effort should be minimized through the reuse of standardized components and interfaces. One of the fundamental pillars of the Web service vision is a brokerage system that enables services to be published to a searchable repository and later retrieved by potential users. One of the subtasks in a service-oriented architecture is service discovery. Service discovery, the identification of existing Web Services that can be used by new Web applications, is one of the most critical problems deterring Web Service (WS) technology. Current solution is based on UDDI catalogue browsing that supports only primitive matching mechanisms and provides no control over the quality of registered services Quality of Service (QoS) is becoming an important criterion for selection of the best available service. Currently the problem is twofold. The Universal Description, Discovery and Integration (UDDI) registries do not have the ability to publish the QoS information, and the authenticity of the advertised QoS information available elsewhere may be questionable. We aim to refine the discovery process through designing a new framework that enhances retrieval algorithms by combining syntactic and semantic matching of services with QoS. We propose a model of QoS-based Web services discovery that combines an augmented UDDI registry to publish the QoS information and a reputation manager to assign reputation scores to the services based on customer feedback of their performance. The Certifier verifies the QoS claims from the Web service suppliers. A discovery agent facilitates QoS-based service discovery using the reputation scores in a service matching, ranking and selection algorithm. The novelty of our model lies in its simplicity and in its coordination of the above mentioned components. The Proposed framework should give Web services consumers some confidence about the quality of services of the discovered Web services

Service recommendation and selection in centralized and decentralized environments.

With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements.In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.N/

Model-driven semantic Web service composition

As the number of available Web services increases there is a growing demand to realise complex business processes by combining and reusing available Web services. The reuse and combination of services results in a composition of Web services that may also involve services provided in the Internet. With semantically described Web services, an automated matchmaking of capabilities can help identify suitable services. To address the need for semantically de-fined Web services, OWL-S and WSML have been proposed as competing semantic Web service languages. We show how the proposed semantic Web service languages can be utilized within a model-driven methodology for build-ing composite Web services. In addition we combine the semantic-based discovery with the support for processing QoS requirements to apply a ranking or a selection of the candidates. The methodology describes a process which guides the developer through four phases, starting with the initial modelling, and ending with a new composite service that can be deployed and published to be consumed by other users.

Bi-Level Selection Model for Web Services Search

Service registries and web service engines are the main approaches for discovering web services. Current service directories are mainly based on Universal Description, Discovery and Integration (UDDI), which is an industry standard for service registries, developed to solve the web service search problem. However, UDDI offers limited search functionalities which may return a huge number of irrelevant services. Another critical challenge in web service search and composition is the selection of web services, to be executed or to be composed, from the pool of matching services. Most of the current service selection proposals apply a weighted sum model (WSM) as an evaluation method for selection of services with the same functionality. In this paper, we propose a Bi-level service selection approach that selects the most appropriate web services from the pool of matching services that considers both the functional and non-functional requirements for service selection. The functional requirements are provided by the user as a set of input parameters provided for and output parameters desired from the web service. The user also provides a set of desired QoS values and the order of their preference for selection. The experimental results demonstrate the efficiency of service search in our bi-level model and the variety of user queries supported

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time

Challenges to describe QoS requirements for web services quality prediction to support web services interoperability in electronic commerce

Quality of service (QoS) is significant and necessary for web service applications quality assurance. Furthermore, web services quality has contributed to the successful implementation of Electronic Commerce (EC) applications. However, QoS is still the big issue for web services research and remains one of the main research questions that need to be explored. We believe that QoS should not only be measured but should also be predicted during the development and implementation stages. However, there are challenges and constraints to determine and choose QoS requirements for high quality web services. Therefore, this paper highlights the challenges for the QoS requirements prediction as they are not easy to identify. Moreover, there are many different perspectives and purposes of web services, and various prediction techniques to describe QoS requirements. Additionally, the paper introduces a metamodel as a concept of what makes a good web service

A Requirement-centric Approach to Web Service Modeling, Discovery, and Selection

Service-Oriented Computing (SOC) has gained considerable popularity for implementing Service-Based Applications (SBAs) in a flexible\ud and effective manner. The basic idea of SOC is to understand users'\ud requirements for SBAs first, and then discover and select relevant\ud services (i.e., that fit closely functional requirements) and offer\ud a high Quality of Service (QoS). Understanding users’ requirements\ud is already achieved by existing requirement engineering approaches\ud (e.g., TROPOS, KAOS, and MAP) which model SBAs in a requirement-driven\ud manner. However, discovering and selecting relevant and high QoS\ud services are still challenging tasks that require time and effort\ud due to the increasing number of available Web services. In this paper,\ud we propose a requirement-centric approach which allows: (i) modeling\ud users’ requirements for SBAs with the MAP formalism and specifying\ud required services using an Intentional Service Model (ISM); (ii)\ud discovering services by querying the Web service search engine Service-Finder\ud and using keywords extracted from the specifications provided by\ud the ISM; and(iii) selecting automatically relevant and high QoS services\ud by applying Formal Concept Analysis (FCA). We validate our approach\ud by performing experiments on an e-books application. The experimental\ud results show that our approach allows the selection of relevant and\ud high QoS services with a high accuracy (the average precision is\ud 89.41%) and efficiency (the average recall is 95.43%)