On the Analysis of Satisfaction for Web Services Selection

A web service is an interface of the software component that can be accessed by standard Internet protocols. The web service technology enables an application to application communication and interoperability. The increasing number of web service providers throughout the globe have produced numerous web services providing the same or similar functionality. This necessitates the use of tools and techniques to search the suitable services available over the Web. UDDI (universal description, discovery and integration) is the first initiative to find the suitable web services based on the requester's functional demands. However, the requester's requirements may also include non-functional aspects like quality of service (QoS). In this paper, the authors define a QoS model for QoS aware and business driven web service publishing and selection. The authors propose a QoS requirement format for the requesters, to specify their complex demands on QoS for the web service selection. The authors define a tree structure called quality constraint tree (QCT) to represent the requester's variety of requirements on QoS properties having varied preferences. The paper proposes a QoS broker based architecture for web service selection, which facilitates the requesters to specify their QoS requirements to select qualitatively optimal web service. A web service selection algorithm is presented, which ranks the functionally similar web services based on the degree of satisfaction of the requester's QoS requirements and preferences. The paper defines web service provider qualities to distinguish qualitatively competitive web services. The paper also presents the modelling and selection mechanism for the requester's alternative constraints defined on the QoS. The authors implement the QoS broker based system to prove the correctness of the proposed web service selection mechanism.

With the proliferation of Web services as a business solution to enterprise application integration, ranking and selecting the best Web services among the providers become an important factor in the success of the business solution. Quality of service (QoS) determines the quality and usability of a service including its price, performance, reliability, integrity, accessibility, availability, interoperability, and security. Given a set of QoS attributes from a variety of sources, it is a challenge to sort through all of them and be able to get the best services that meet QoS requirement. In this paper, we describe a novel method by which Web services can be ranked and selected automatically based on a number of observed QoS parameters and feedback responses learned from prior knowledge. This new approach treats the observed Web services QoS attributes and target Web services relationship, represented by a matrix, as a statistical problem. Using singular value decomposition (SVD) technique, and an user assisted weighting system, implicit higher order correlations among Web services and their associated QoS attributes are extracted and used to estimate the selection of recommended Web services.

With the increasing number of Web services, the Semantic Web research community is moving toward enhancing procedures so that clients may effectively discover appropriate Web services that maximally satisfy not only functional but also non-functional requirements. Using purely syntactic approaches to discover Web services has limited efficiency. Therefore, semantic matchmaking registries can play an important role in providing better results. However, most of the semantic registries make decisions based on only functional requirements. Due to the proliferation of Web services that have similar functionality, there is a need to further filter Web services according to Quality of Service (QoS) specifications. This work introduces a rich QoS ontology inspired from the World Wide Web Consortium that defines relationships among QoS attributes. The QoS attributes defined here are mainly used to define network related characteristics. However, the work can be extended by defining QoS ontologies for other domains. Additionally, we extend the test collection OWLS-TC according to the QoS ontology. The extension is intended to integrate quantified values for QoS attributes into Web service descriptions. A matchmaking algorithm based on similarity measurements is presented. Moreover, deterministic and adaptive parameter control techniques are introduced to guide the requester, when needed, to adjust QoS specifications so better similarity can be achieved. Parameter control algorithms integrate Pellet, an OWL reasoner, to reason about dependencies among QoS concepts in the QoS ontology.

Web services provide a promising solution to an age old need of fast and flexible information sharing among people and businesses. One of the key research issues in web services is selection of most pertinent web services. Selection of web service has become a tedious job because of the increasing number of service providers providing services with similar functionality. Therefore, a framework for selection and discovery of web service that can meet the non-functional requirements is needed. In this work, we have developed a selection tool (WSS-NFP) for web services which can rank services based on their non functional properties such as performance, delay etc. We have used soft computing techniques for selection and discovery of web services based on non functional properties. These properties are fuzzy in nature. A tool is developed and implemented that is generic in nature and can be customized for any application domain. As a core of this tool, it consists of Fuzzy System for ranking web services and Neural Network for fine tuning the membership functions for linguistic terms. Appropriate and fine tuned membership functions help in minimizing the output error measure and maximizing the performance index of the proposed tool. This tool is basically a Neuro-Fuzzy system for ranking web services of any domain.

Web service is an emerging technology in this decade for its openness and loosely coupled interaction among systems. Due to the proliferation of web services, more number of web services is available for similar functionality. QoS deals about the non-functional information of a Web services. Furthermore, QoS plays a vital role for differentiating web services which has similar functionality. Predicting the QoS value of a web service is an important process for web service discovery and selection. Due to the different network environment includes bandwidth, capacity and delay, the QoS value of a web service is differs from user to user. Therefore, QoS prediction is imperative for a recommendation system to provide best services based on QoS values. To achieve this, an enhanced collaborative filtering approach is introduced. In this approach, the PCC (Pearson Correlation Coefficient) is calculated for identifying the similar user and items. To increase the prediction accuracy, the user’s personalized degree of similarity is calculated and the Top-k users and items are identified. The confidence value computation helps to predict the missing values in the user-item matrix. By systematic integration of user-based and item-based methods, the final QoS values of a web services is predicted. The system has been tested with the invocation of real world web services, and the efficiency of the proposed approach is compared with the existing approaches. The MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) between the proposed and existing approaches shows the accurate prediction of the proposed approach.

Virtual enterprise is a temporary network of independent companies or enterprises that can quickly bring together to fulfill a value-added task. With the development of Web service technologies, the enterprise business systems can be encapsulated as Web services. Establishing a virtual enterprise is actually a process of Web services composition. As more and more Web services are available, many Web services with different quality of service (QoS) can be found for tasks of the composite Web service. Thus, a significant research problem in Web service composition is how to select the correct Web services for tasks such that the composite Web service gives the best overall quality. In this paper, we propose an approach to achieve Web services selection efficiently with excellent comprehensive quality. Firstly, a comprehensive evaluation model based on generic QoS (GQoS) and domain QoS (DQoS) of composite Web service is established. Secondly, a novel optimization algorithm named culture max–min ant system (C-MMAS) is constructed to solve the problem of Web service selection based on DGQoS. Simulations show that when used in Web service selection, C-MMAS is better than ant colony optimization algorithm and MMAS in efficiency.

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

The web service has widely spread in recent years which is showed by the large number of published web services. However, users may not always find their needs in one web service which has consequently spawned a composition of web services. This composition lead to many task difficulties including the selection of web services, which also consists of choosing the best QoS-based services among other similar services provided. From this perspective, there are several approaches to the selection of web services, however, this remains deficient at the level of execution time due to the rage number of competing web services. In this paper, we propose a new selection approach based on prefiltering and Skyline, this approach is tested and improved using a real dataset. We used two steps to solve this problem, prefiltering and Skyline. The first step is the use of K-Means clustering to bundle web services with similar Quality of Service (QoS) properties under one umbrella is explored. The second one is defining the most dominant web service using the Branch-and-Bound Skyline algorithm.

While Web service models become increasingly popular and many service providers start to offer all kinds of Web services, the quality of service (QoS) for Web services has become an important factor for service requesters to consider during the selection process of appropriate Web services. In this paper, we present an evaluation and selection framework (CosmosQoS) for QoS-required Web services. A novel architecture and reputation appraisal model to monitor, store and evaluate the physical usage and post-condition of requested Web services in a requester-located network is proposed to better support service requesters such that they can select suitable Web services based on their QoS requirements, trustworthiness of Web services, and effect of network connection and configuration between service requesters and providers.

Background/Objectives: The primary objective of the study is to evaluate the performance of Composite Web Service based on the parameters relating to Quality of Service by using statistical methodologies. Methods/Statistical Analysis: By using Web Service Crawler Engine we have collected data on eleven parameters of interest from a more number of Web Services. Among the eleven parameters we have selected six parameters of importance using the sampling of thirty Web Services. We have constructed the weight matrix for the parameters. By using Web Services Relevancy Function ranking has been done for three different scenarios. Using Spearman's Rank Correlation Coefficient we have compared the scenarios. Findings: The graphical representation of the three scenarios shows varying pattern. The Spearman's Rank Correlation coefficient of the Web Services Relevancy Function varied widely among the sampled Web Services. F-Test revealed that there are no significant differences in respect of weights of Quality of Web Services for three different scenarios. We concluded that weight assigned for Quality of Service parameters in Scenario I is preferred than the scenarios II and III through the evaluations. Application/Improvements: The technique developed in this research paper can be applied for the comparison of Web Services during discovery to enhance the performance of the Composite Web Service.

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.

The development, registration, discovery, and invocation of quality Web services are vital for the successful implementation of applications using Web services. Considerable research focuses on quality for Web services. Unfortunately, current research on Quality of Service (QoS) for Web services is concentrated on service users and the implementation stage. This research highlights the importance of incorporating QoS at the design and development stages; the authors propose the introduction of QoS at the same time as functional requirements. However, Web Service Description Language (WSDL) describes the functional elements of a Web service, and QoS is significant for this description. Therefore, the authors propose an extension to the WSDL through a generic QoS metamodel, incorporating QoS specifications into the functionalities. This paper begins by defining the required QoS specifications for the development of quality Web services and explores the potential of the Unified Modeling Language as a technique and notation to specify QoS. To properly integrate QoS in the design, the authors propose extensions to the existing UML QoS profile. The paper concludes with the evaluation of the proposed framework and summarises its advantages.

The main objective of this paper is to design and develop a Self-Adaptable Media Service Architecture (SAMSA) for providing reliable dynamic composite multimedia service through policy-based actions. The multimedia services such as media retrieval, transcoding, scaling and display services are combined based on the preferences of the user to create a dynamic composite multimedia service called as Video-on-Demand service. Such distributed multimedia services deployed using Service Oriented Architecture (SOA) can be accessed in heterogeneous environments that are prone to changes during run-time. To provide reliable and adaptive multimedia services, a powerful self-adaptable architecture with dynamic compositions of multimedia services is necessary to adapt during run-time and react to the environment. The adaptability in this proposed architecture is achieved by enabling the service providers to Monitor, Analyze and Act on the defined policies that support customization of compositions of multimedia services and guarantee the Quality of Service (QoS) provisioning. The Media Service Monitor (MSM) observes the business and quality metrics associated with the multimedia services during run-time. The monitored results are analyzed by Monitored Results Analyzer (MRA) which identifies the type and location of the fault. The Adaptive Media Service Manager (AMSM) takes corrective actions based on the monitored results, through the policies defined as an extension of WS-Policy (Web Service--Policy framework). The effectiveness of the proposed Self-Adaptable Media Service Architecture (SAMSA) has been evaluated on Dynamic Composite Real-time Video-on-Demand Web Service (DCRVoDWS) for a maximum of 200 simultaneous client's requests. The analysis of results shows that the proposed architecture provides better improvement on reliability, response time and user satisfaction.

With the rapid growth of functionally identical web services, Quality of Service (QoS) plays vital role for deciding the most suitable services for consumers. Although, QoS for web services has gained extensive attention in the literature, most of the existing efforts are unable to consider the multiperspective QoS of web services. Unlike conventional software paradigms, web services are provided, developed and used by different users such as provider, consumer and broker. As a result, QoS requirements vary from user to user. Existing researches are unable to provide standard solution, which can deal with what service providers, consumers and broker should expose in service description as QoS parameters. Therefore, in this book chapter, a multiperspective PCB-QoS classification of non-functional parameters along with new QoS parameters, i.e. access rate and Overall Aggregated Effective Quality of Service (OAEQoS) is presented. Due to the extensible nature of PCB-QoS classification, it can be extended to accommodate more number of QoS parameters. This QoS classification is based on the idea of partitioning the generic QoS parameters according to multi-user’s perspective. An Average Real Value Sum Method (ARSM) based on default priority constraints is also proposed for ranking of web services that uses the presented QoS classification. With the help of proposed approach, the user can specify the service requirements without taking the hurdle of specifying the weights for each QoS parameter during web service operations. It also prevents the services from malicious service provider to publish the incorrect and inaccurate QoS information. Experimental evaluation shows the effectiveness of proposed approach over the existing approach.