QoS-based Web Service Research Articles

Qos-based web service selection using time-aware collaborative filtering: a literature review

Qos-based web service selection using time-aware collaborative filtering: a literature review

A QoS Metric Approach for Web Service Pertinence for the Cloud

Background: Cloud computing is a very emerging technology today and playing an important role to provide the services through internet. Users are getting enormous benefits like high accessability of available application resources, faster time to market, fast development, deployment, lower start-up, operations cost and much more. Cloud offers us to utilize the benefits through web services. Objective: The degrading factors such as low Quality of Service, security factors, trustless and less resource management in cloud leads to service suitability issue. In service oriented computing finding an efficient QoS based web service on the web is very challenging task. Methods: A quality of service metric approach is proposed to identify the suitability of web services and ensuring the deployability in the cloud era. The suitability of web service is examined for its suitability cases (simple suitable, average suitable and best suitable) with respect to the quality of service attribute. Results: The experimental setup, first the suitability scores calculated with the essential non-functional quality of service attribute information is identified from the set of services. However, the verification process expressed using the state transition diagram. Conclusion: The proposed system is simulated using JFLAP-a java package tool and the result shows the service suitability for the cloud. In this context the efficiency of identifying service suitability for deploying in the cloud gets verified using automated theory without affecting the performance of the cloud service.

AN APPROACH WEB SERVICE SELECTION BY QUALITY CRITERIA BASED ON SENSITIVITY ANALYSIS OF MCDM METHODS

Context. The problem of QoS based Web service from the list of Web services with equal or similar functionality was considered. This task is an essential part of the processes of finding, discover, matching and using Web services on the Internet due to the numerous offerings of Web services with equal or similar functionality. The reasonable selection of a suitable Web service takes into account a lot of user’s quality requirements, such as response time, throughput, reliability, cost, etc. Such a task is usually formulated as an MCDM problem, in which the parameters are the Web service quality factors and the importance degree of these factors. The object of this research is a process of selection Web services using MCDM methods, taking into accounts the user’s preferences and requirements to the Web service quality characteristics. The subject of the research is the LSP method, which, in addition to the degree of importance of the criteria used in all MCDM methods, simulates the user’s reasoning about quality, taking into account, in particular, such characteristics of the criteria as mandatory, sufficiency, desirability, simultaneity and substitutability. Objective. The objective of the work is to develop an approach for comparing the result of using the LSP method with the results of using other MCDM methods. Method. A method for calculating the weights of input criteria that are not always explicitly specified in the LSP method was proposed. For this, the conjunctive coefficients of impact are used, which are calculated as a result of the sensitivity analysis of the Web service generalized quality criterion to changes the partial quality criteria. This method underlies the proposed approach to comparing the efficiency of the LSP method with other MCDM methods, which consists of using the obtained weights as the weights of the input criteria for the MCDM methods. Results. The developed method and approach was verified experimentally. The Web service ranking produced by the LSP method was compared with the ones produced by SAW, AHP, TOPSIS and VIKOR methods. This comparison confirmed the efficiency of the proposed method and approach. Conclusions. From the obtained results of comparing the LSP method and the MCDM methods considered in this study, it follows that the proposed method and approach provide the equivalent input conditions for these methods as for the LSP method, which is a necessary condition for the correct comparison of MCDM methods. The use of the proposed approach made it possible to study the sensitivities of the considered MCDM methods. In practical applications, this approach can be used to select a suitable MCDM method. The proposed method can be useful for creating professional evaluation systems in which it is necessary to assess the importance (weights) of tens and hundreds of quality criteria.

Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

An Improved Approach for QoS Based Web Services Selection Using Clustering

An Improved Approach for QoS Based Web Services Selection Using Clustering

ПІДХІД ДО ВИБОРУ ВЕБСЕРВІСІВ НА ОСНОВІ ЯКОСТІ З УРАХУВАННЯМ ВПОДОБАНЬ КОРИСТУВАЧА

This paper is devoted to applying the LSP method for Web service selection based on quality characteristics and the user’s preferences. The purpose of this work is to develop an approach for the Web service selection, which, based on a reasonable choice of Web service quality attributes and the use of the LSP method, allows for a deeper consideration of consumer preferences. The efficiency the LSP method for QoS-based Web service selection is conformed. The novelty of this work is that the LSP method was fully used for QoS-based Web service selection in comparison to the publications authors are aware of. Keywords: Web service, quality attributes, LSP method, aggregation framework, multicriteria decision-making method.

A design of intelligent QoS aware web service recommendation system

In web services environment, making an appropriate service recommendation to improve the web service composition ability and decrease composition cognition is a promising approach. Web service filtering is one of the essential technique for recommending QoS based web services. Collaborative Filtering (CF) plays major role in Web service recommendation which intends to predict missing QoS values of web services. QoS prediction techniques, emphasize more on user’s personalized influence of services and service QoS factors such as response time and throughput. The proposed model merges the location preference and personalized usage influences of the requester with Privacy, Demand, Satisfaction and Retention level assessment. Neuro Fuzzy Logic (NFL) is introduced to improve classification accuracy to intelligently identify the untrustworthy Web services. Intelligent Neuro Fuzzy Collaborative filtering (INFCF) for QoS aware web service recommendation is designed to enhance the QoS in web service recommendation.

QoS Based web services selection using a Bi-Level model

QoS Based web services selection using a Bi-Level model

An Agent Architecture for Qos-Based Web Service Composition Using the Skyline Algorithm

&lt;p&gt;Web service composition is a concept based on the built of an abstract process, by combining multiple existing class instances, where during the execution, each service class is replaced by a concrete service, selected from several web service candidates. This approach has as an advantage generating flexible and low coupling applications, based on its conception on many elementary modules available on the web. The process of service selection during the composition is based on several axes, one of these axes is the QoS-based web service selection. The Qos or Quality of Service represent a set of parameters that characterize the non-functional web service aspect (execution time, cost, etc...). The composition of web services based on Qos, is the process which allows the selection of the web services that fulfill the user need, based on its qualities. Selected services should optimize the global QoS of the composed process, while satisfying all the constraints specified by the client in all QoS parameters. In this paper, we propose an approach based on the concept of agent system and Skyline approach to effectively select services for composition, and reducing the number of candidate services to be generated and considered in treatment. To evaluate our approach experimentally, we use a several random datasets of services with random values of qualities.&lt;/p&gt;

An Advanced QoS-Based Web Service Selection Approach

QoS-based web service selection is an important aspect for achieving efficient operations for web service systems. The aim of web service selection is to select an appropriate concrete web service with the best quality of service (QoS) for each abstract web service in a workflow. One way to resolve this problem is to calculate the Pareto optimal solutions which have the better QoS values for some QoS attributes while having at least equivalent values for others. Although a lot of approaches can do that, they do not guarantee the result precision or have prohibitively large overhead. In this paper, we present an Advanced A-Fully Polynomial Time Approximation Scheme (A2-FPTAS) by the unequal local error bound to balance the precision and the overhead. Experimental results are presented to show the efficiency of this approach.

QoS-based Web Service Composition Applying an Improved Genetic Algorithm (IGA) Method

In recent years, it has been made possible to compose exiting services when a user's request cannot be satisfied by a single web service. Web service composition is faced with several challenges among which is the rapid growth in the number of available web services leading to increased number of web services offering the same functionalities. The difference between similar services is Quality of Service (QoS) consisting of various non-functional factors such as execution time, availability, security, etc. As a result, multiple choices are possible in making a composition plan. Among numerous plans, selecting a composition plan that fulfills customer's requirements has become an important and time-consuming problem. In this paper, the researchers propose a semi-heuristic genetic algorithm that is a combination of both a heuristic method and the genetic algorithm. This heuristic method changes chromosomes based on unsatisfied constraints. Research findings show that the proposed method can be applied to find a composition plan that satisfies user's requirements more efficiently than other methods.

CluCF: a clustering CF algorithm to address data sparsity problem

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren't fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.

Context Aware QOS based Web Service Discovery

This work concentrates on context of the execution environment in service selection process due to dynamically varying QoS attributes in service execution environment. In this paper, a context aware QoS Web Service(WS) description and discovery is proposed to overcome the issues related to QoS requirements in WS. In the proposed scheme, OWL-Q with OWL CONtext ONtology (CONON) ontology is modeled to provide the WS description with context aware QoS descriptions. To discover the most appropriate WS based on context and QoS descriptions, a context aware QoS semantic matchmaking algorithm is newly proposed based on Constraint Satisfaction Optimization Approach (CSOP). An efficient User Priority based Ranking (UPR) mechanism is performed based on user preferences to rank the matched WSs. Consequently, precision and recall of proposed context aware QoS WS discovery is compared with the existing QoS WS discovery and conclude that proposed approach achieves high precision and recall than existing QoS based approach.

Fruit Fly Optimization Algorithm for Network-Aware Web Service Composition in the Cloud

Service Oriented Computing (SOC) provides a framework for the realization of loosely coupled service oriented applications. Web services are central to the concept of SOC. Currently, research into how web services can be composed to yield QoS optimal composite service has gathered significant attention. However, the number and spread of web services across the cloud data centers has increased, thereby increasing the impact of the network on composite service performance experienced by the user. Recently, QoS-based web service composition techniques focus on optimizing web service QoS attributes such as cost, response time, execution time, etc. In doing so, existing approaches do not separate QoS of the network from web service QoS during service composition. In this paper, we propose a network-aware service composition approach which separates QoS of the network from QoS of web services in the Cloud. Consequently, our approach searches for composite services that are not only QoS-optimal but also have optimal QoS of the network. Our approach consists of a network model which estimates the QoS of the network in the form of network latency between services on the cloud. It also consists of a service composition technique based on fruit fly optimization algorithm which leverages the network model to search for low latency compositions without compromising service QoS levels. The approach is discussed and the results of evaluation are presented. The results indicate that the proposed approach is competitive in finding QoS optimal and low latency solutions when compared to recent techniques.

A collaborative framework of web service recommendation with clustering-extended matrix factorisation

QoS-based web service recommendation is an important technique to select suitable services to users. In this paper, we aim to achieve superior recommendation accuracy by leveraging the known QoS records. To achieve this goal, we employ the clustering algorithm and Matrix Factorisation model MF, and propose a collaborative framework of web service recommendation. Using the clustering algorithm, we cluster users and services into different clusters based on their QoS records, and identify similar cluster centres for each user and each service. We propose two clustering-extended MF models, i.e., service clustering-extended MF model SC-EMF and user clustering-extended MF model UC-EMF. In both models, the QoS values are predicted by two parts. One is the invocation experience of the target service or user, and the other is that of the similar centres. The experimental results show the effectiveness of our models.

Location and Qos based Web Service Recommender System

Web services are the components usually called as integrated software components which support the interoperable machine-to-machine interaction over any network. In today‟s world, the availability of web services is increasing actively. A recommender system is presented that helps the users to select services with good Quality-of-Service (QoS) performance. This recommender system extracts the information from two parameters called location and QoS values. The basic idea is to predict Web service QoS values and recommend the best one for users based on historical Web service QoS records. Firstly, Locations are clustered using Euclidean distance and then the similarity of users of that particular location is calculated. Likewise, Web services are clustered based on the similarity and then prediction is done based on the clusters formed. After Clustering, the missing data is being predicted using the Pearson correlation of nearest neighboring approach. Prediction is done from both service perspective and user perspective. Once missing data is obtained, services are ready to be recommended. Lastly, users are recommended with services which are being used by them in the history with better QoS values. QoS values (throughput and response time) help in finding best services to be utilized by the user. The system is evaluated using MAE, which is majorly used to evaluate the predicted values. General Terms Item based collaborative filtering techniques; User based collaborative filtering techniques.

Enhancing Fuzzy Topsis for web service selection

With proliferation of web services, quality-of-service QoS capability is becoming a significant factor for distinguishing services. QoS includes various non-functional issues such as performance, reliability and availability, etc. The functional requirements are assured through Semantic Matchmaking algorithm whereas the non functional requirements are guaranteed though QoS factors. Selection of a web service has become very crucial due to the availability of multiple web service with similar functionality. To solve this issue there is a need to distinguish the services based on their non-functional criteria QoS. This paper proposes an enhanced QoS-based semantic web service selection and ranking solution with the application of Fuzzy Topsis method. Further, this paper proposes a new dynamic replication algorithm to increase the availability of the service and decrease network delays. This is demonstrated using a diabetes monitoring system.

Efficient Discovery and Ranking of Web Services Using Non-functional QoS Requirements for Smart Grid Applications

Abstract Web service discovery is the important process and traditional methods use keyword-based search to locate the appropriate web service. This method may not yield good results. Non-functional QoS parameters of a web service play a vital role in ranking a web service. Moreover, standards such as UDDI, WSDL and SOAP have potential of providing QoS based web service discovery. In this paper, a new method is proposed to rank the web services. The proposed method computes relevancy function value of the web services and normalization is performed. This normalized result is used to rerank the web service. The selection of appropriate web service provide user to access smart grid over internet. The experimental results show that the proposed method is effective in selecting appropriate web services than traditional methods

Personalised QoS-based web service recommendation with service neighbourhood-enhanced matrix factorisation

Web service recommendation has been an urgent task with the wide adoption of services in cloud computing and enterprise information systems, and Quality of Service (QoS) prediction takes a critical role. In this paper, based on probabilistic matrix factorisation model, we propose three service–neighbourhood enhanced prediction models. In the first model, after identifying the neighbours sharing similar QoS records, we learn the predicted value both utilising feature vectors of the active service and its neighbours. Then, in the second model, the learning process is enhanced through the integration with each neighbour's weight computed from the similarity. Afterwards, in the final model, we emphasise the special roles of services possessed by the same company with the active service, with minimising the difference of feature vectors between them. Further, we propose a unified recommendation framework, involving in the three models. Finally, we conduct extensive experiments showing our models' effectiveness.

A Web service QoS prediction approach based on time- and location-aware collaborative filtering

In QoS-based Web service recommendation, predicting quality of service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. CF algorithms can be divided into two main categories: memory-based and model-based algorithms. Memory-based CF algorithms are easy to implement and highly effective, but they suffer from a fundamental problem: inability to scale-up. Model-based CF algorithms, such as clustering CF algorithms, address the scalability problem by seeking users for recommendation within smaller and highly similar clusters, rather than within the entire database. However, they are often time-consuming to build and update. In this paper, we propose a time-aware and location-aware CF algorithms. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS data set. Experimental results show that our approach is capable of addressing the three important challenges of recommender systems---high quality of prediction, high scalability, and easy to build and update.