An effective method for clustering-based web service recommendation

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.

We study the problem of personalized Quality of Service (QoS) estimation for web services. State-of-the-art methods use matrix factorization or collaborative prediction to estimate web service response times and throughput for each user based on partial measurements collected from past invocations. We point out that in reality, both the response times and through-put of web services follow highly skewed distributions. In this case, the conditional mean QoS estimates generated by traditional matrix completion approaches can be heavily biased toward a few outliers, leading to poor web service recommendation performance. In this paper, we propose the Quantile Matrix Factorization (QMF) technique for web service recommendation by introducing quantile regression into the matrix factorization framework. We propose a novel and efficient algorithm based on Iterative Reweighted Least Squares (IRLS) to solve the QMF problem involving a non-smooth objective function. We further extend the proposed QMF approach to take into account user and service side attributes. Extensive evaluation based on a large-scale QoS dataset has shown that our schemes significantly outperform various state-of-the-art web service QoS estimation schemes in terms of personalized recommendation performance.

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.

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.

Now-a-days the web services are uprising technology which appeals lots of attention from both academic and industry. The quality of service has been one of the major challenges in the web services. If the web services become operational then the service providers with Service Level Agreement (SLA) need to consider and monitor with respect to the quality of web services and SLA. The most important factor is that to monitor and prevent the SLA intrusion in the composite web services. In the existing work the adaptation is done only at the instance level of the composition, for each level of composition the adaptation is done in each instance. Then the aggregated SLO's are defined over the number of instances which are out of scope and also the adaptation is not considered as permanent. In this PERvent (prediction and prevention based on event monitoring) framework is not supported for large number of adaptation action per the checkpoint because it is fully based on the number of checkpoints, and also it supports limited structural adaptation in the composition. The proposed work is to exclude these drawbacks by introducing the framework called Quality based dynamic composition (QDC) framework for monitoring the SLA parameter as well as monitoring Service Quality (QOS) Parameter for composite web service and also we will discuss the experimental results to prove how these approaches to monitor the Quality of Service (QOS) parameters for the composed web service.

How to assure Quality of Service (QoS) of the third-party services is very important for the SOA. Effective monitoring technique towards QoS, which is an important measurement for third-party service quality, is necessary to ensure quality of Web service. Current monitoring approaches do not consider the influences of environment factors such as the position of server, user usage, and the load at runtime. Ignoring these influences, which do exist among the monitoring process, may cause existing monitoring approaches producing unpredictable monitoring results. In order to overcome this limitation, this paper proposes a novel Web Service QoS (WS-Qos) monitoring approach sensitive to environmental factors called weighted Bayesian Runtime Monitor (wBSRM) based on weighted naïve Bayesian classifiers and Term Frequency-Inverse Document Frequency (TF-IDF) algorithm. wBSRM constructs weighted naïve Bayesian classifier by learning a part of samples to classify the monitoring results. The results meeting QoS standard are classified as [Formula: see text] and the one that does not meet is classified as [Formula: see text]. Classifier can also output ratio between posterior probability of [Formula: see text] and [Formula: see text], and consequently the analysis can lead to three monitoring results including [Formula: see text], [Formula: see text] or inconclusive. A set of dedicated experiments are conducted to validate wBSRM. The experiments are based on a public dataset and a simulated dataset under the given standard. The experimental results demonstrate that wBSRM is better than previous approaches.

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.

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.

Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.

For Web services to expand across the Internet, users need to be able to efficiently access and share Web services. The authors present a query infrastructure that treats Web services as first-class objects. It evaluates queries through the invocations of different Web service operations. Because efficiency plays a central role in such evaluations, the authors propose a query optimization model based on aggregating the quality of Web service (QoWS) parameters of different Web services. The model adjusts QoWS through a dynamic rating scheme and multilevel matching in which the rating provides an assessment of Web services' behavior. Multilevel matching allows the expansion of the solution space by enabling similar and partial answers.

A probabilistic approach to modeling and estimating the QoS of web-services-based workflows

Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns

With the growing popularity of Service-Oriented-Computing (SOC) architecture, the number of Web services on the internet is increasing rapidly. When faced with a large number of candidate services with similar functionalities, personalized Web service recommendation is becoming an important issue. Quality-of-Service (QoS) is usually used to characterize the non-functional properties of Web services. Thus accurate QoS prediction is an important step in the service recommendation. In this paper, we propose a Cluster Feature based Latent Factor Model (CFLFM) for QoS prediction. First, we cluster users and services into several groups based on history records, respectively. We assume that users or services in the same cluster share some latent features. By incorporating this kind of information, we design an integrated latent factor model. Finally, we conduct comprehensive experiments on a real-world Web service dataset. The experimental results show that our approach can achieve higher QoS prediction accuracy than other competing approaches.

Web service composition (WSC) is the task of combining a chain of connected single services together to create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied to represent nonfunctional properties of web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition, as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, which can incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of finding a composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novel planning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal and numerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system and conduct extensive experiments on large web service repositories. The experimental results show that our proposed approach largely outperforms existing ones in terms of solution quality and is efficient enough for practical deployment.