Quality Of Service Prediction Approach Research Articles

Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization

Owing to the ever-growing popularity of mobile computing, a large number of services have been developed for a variety of users. Considering this, recommending useful services to users is an urgent problem that needs to be addressed. Collaborative filtering (CF) approaches have been successfully adopted for services recommendation. Nevertheless, the prediction accuracy of the existing CF approaches is likely to reduce due to many reasons, such as inability to use side information and high data sparsity, which further lead to low quality of services recommendation. In order to solve these problems, some model-based CF approaches have been proposed. In this paper, we propose a novel quality of service prediction approach based on probabilistic matrix factorization (PMF), which has the capability of incorporating network location (an important factor in mobile computing) and implicit associations among users and services. First, we propose a novel clustering method that is capable of utilizing network location to cluster users. Based on the clustering results, we further propose an enhanced PMF model. The proposed model also incorporates the implicit associations among users and services. In addition, our model incorporates the implicit relationships between the users and the services. We conducted experiments on one real-world data set, and the experimental results show that our model outperforms the compared methods.

Your neighbors alleviate cold-start: On geographical neighborhood influence to collaborative web service QoS prediction

Predicting the unknown quality of service(QoS) for an active user who has not previously accessed a Web service plays a fundamental role in supporting appropriate service selection, high quality service composition and reliable distributed system construction. Existing Web service QoS prediction methods suffer from the problems of data sparsity and cold-start, which dramatically degrade prediction accuracy and even impede their applicability in real scenarios. Additionally, the potentially positive but inconspicuous relation between geographical region and QoS rating interaction among users and Web services has been underestimated in previous studies. In contrast to those studies, we propose a collaborative Web service QoS prediction approach that incorporates the knowledge of geographical neighborhoods. By analyzing the geographical relationships in the real-world dataset WSDream, we observe that users and Web services are positively correlated with their geographical neighbors. Based on this observation, we first design a bottom up neighborhood clustering method for correlating geographical neighbor selection. Then, we construct two diversified similarity neighborhood regularization terms and systemically integrate them into a matrix factorization model, which achieves the knowledge transfer of geographical neighborhoods in improving QoS prediction accuracy. Experimental results have demonstrated that our approach performs more efficiently than existing methods with respect to accuracy, as well as alleviating the data sparsity and cold-start issues.

Personalized and Accurate QoS Prediction Approach Based on Online Learning Matrix Factorization for Web Services

Quality of Service (QoS) prediction has played an important role in service computing. However, in the real-world scenario of Web service, many user-observed QoS values are unknown and vary over time. In order to provide high accurate and efficient QoS prediction performance for Web services, we propose a personalized and accurate QoS prediction approach namely PAOMF. Our prediction model is built by employing matrix factorization and online stochastic gradient descent algorithm. Extensive experiments are conducted on real world public datasets, which demonstrate the effectiveness and efficiency of our proposed approach.

A novel approach for QoS prediction based on Bayesian combinational model

As an important factor in evaluating service, QoS (Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However, due to the great volatility of services in Mobile Internet environments, such as internet of vehicles, Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place, which is considered a more effective way to assure quality. However, Current QoS prediction approaches neither consider the highly dynamic of Web services, nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time, throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.

TAP: A personalized trust-aware QoS prediction approach for web service recommendation

With the rapid development of service-oriented computing, cloud computing and big data, a large number of functionally equivalent web services are available on the Internet. Quality of Service (QoS) becomes a differentiating point of services to attract customers. Since the QoS of services varies widely among users due to the unpredicted network, physical location and other objective factors, many Collaborative Filtering based approaches are recently proposed to predict the unknown QoS by employing the historical user-contributed QoS data. However, most existing approaches ignore the data credibility problem and are thus vulnerable to the unreliable QoS data contributed by dishonest users. To address this problem, we propose a trust-aware approach TAP for reliable personalized QoS prediction. Firstly, we cluster the users and calculate the reputation of users based on the clustering information by a beta reputation system. Secondly, a set of trustworthy similar users is identified according to the calculated user reputation and similarity. Finally, we identify a set of similar services by clustering the services and make prediction for active users by combining the QoS data of the trustworthy similar users and similar services. Comprehensive real-world experiments are conducted to demonstrate the effectiveness and robustness of our approach compared with other state-of-the-art approaches.

Web service QoS prediction by neighbor information combined non-negative matrix factorization

Strongly promoted by the development of Service-Oriented Computing and Cloud Computing technologies, a large number of functionally equivalent web services emerge on the Internet. Quality of Service (QoS) is becoming a key factor to distinguish different web services. Although many Collaborative Filtering (CF) based approaches are recently proposed to predict the QoS of web services, the prediction accuracy are not satisfactory, since they rarely take full use of the neighbor information and latent feature information contained in the historical QoS data. Especially when the realworld QoS data is highly sparse, the previous works fail to detect the actual relationships between services. In this paper, we present a novel hybrid web service QoS prediction approach that systematically combines the memory-based CF and model-based CF. Firstly a non-negative matrix factorization model for web service QoS prediction is presented. Then an Expectation-Maximization (EM) based approach is designed to learn the model for making further prediction. The service neighbor information, which integrates the direct similarity and transitive indirect similarity of services to handle the data sparsity problem, is introduced into EM based learning process to make the prediction results more accurate. Large-scale real-world experiments are conducted using the WSDREAM QoS dataset. The experimental results demonstrate that our approach can achieve better prediction accuracy than other state-of-the-art approaches.

Online Optimization of Collaborative Web Service QoS Prediction Based on Approximate Dynamic Programming

More recently, with the increasing demand of web services on the World Wide Web used in the Internet of Things (IoTs), there has been a growing interest in the study of efficient web service quality evaluation approaches based on prediction strategies to obtain accurate quality-of-service (QoS) values. However, it is obvious that the web service quality changes significantly under the unpredictable network environment. Such changes impose very challenging obstacles to web service QoS prediction. Most of the traditional web service QoS prediction approaches are implemented only using a set of static model parameters with the help of designer's a priori knowledge. Unlike the traditional QoS prediction approaches, our algorithm in this paper is realized by incorporating approximate dynamic programming- (ADP-) based online parameter tuning strategy into the QoS prediction approach. Through online learning and optimization, the proposed approach provides the QoS prediction with automatic parameter tuning capability, and prior knowledge or identification of the prediction model is not required. Therefore, the near-optimal performance of QoS prediction can be achieved. Experimental studies are carried out to demonstrate the effectiveness of the proposed ADP-based prediction approach.

Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services

Recently, more and more traditional services are being migrated into a cloud computing environment that makes the quality of service (QoS) becomes an important factor for service selection and optimal service composition while forming cross-cloud service applications. Considering the nonlinear and dynamic property of QoS data, it is so difficult to achieve dynamic prediction while designing a QoS prediction method with unsatisfactory prediction accuracy. It is thus desirable to explore how to design an effective approach by incorporating some intelligent techniques into the QoS prediction method to improve prediction performance. In this paper, motivated by the adaptive critic design and Q-learning technique, we propose a novel QoS prediction approach to serve this purpose through the combination of fuzzy neural networks and adaptive dynamic programming (ADP), i.e., an online learning scheme. This approach extracts fuzzy rules from QoS data and employs the ADP method to parameter learning of the fuzzy rules. Moreover, we provide a convergence boundedness result for our proposed approach to guarantee the stability. Experimental results on a large-scale QoS service data set verify the prediction accuracy of our proposed approach.

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.

A heuristic adaptive QoS prediction scheme in single-hop passive star coupled WDM optical networks

In this paper, a novel quality of service (QoS) prediction framework is proposed to accommodate different applications with various QoS requirements. The purpose of the proposed structure is to estimate, given the QoS requirements of different applications, the number of them can be satisfied according to the predefined QoS level and the number of them have to be rejected or accepted under lower level. Moreover, this structure is able to control the network behavior when the new application with certain QoS requirements requesting for the connection. The framework consists of four modules. QoS Requirements Monitoring detects the changes in the supplied QoS. QoS Prediction performs at real-time to estimate whether the new QoS can be adapted to the current QoS. QoS Violation is dedicated to detect the violation. And a module of QoS Estimation is used to make the decision whether the hopeless new traffic is refused or accepted with acceptable QoS requirements. Therefore, this structure is able to control network behavior when the surrounding environment changes. Numerical and simulation results obtained suggest that the proposed predictive scheme is a promising approach for real time QoS prediction.

Web Service QoS Prediction Approach

服务消费者在选择服务之前,通常需要基于其他消费者的经验对未使用过的服务的质量进行预测.考虑到不同服务消费者对同一服务的服务质量的感受之间可能存在较大的差别,提出了一种QoS(quality of service)预测方法.该方法以消费者的历史经验为基础,计算消费者之间以及服务之间的相似程度,并以此相似度为基础对消费者并未使用过的服务的QoS进行预测.实验结果表明,这种方法可以显著提高Web服务质量预测的准确性.;Consumers need to make prediction on the quality of unused Web services before selecting. Usually, this prediction is made based on other consumers’ experiences. Being aware of different QoS (quality of service) experiences of consumers, this paper proposes a QoS prediction approach. This approach makes similarity mining among consumers and QoS data, and then predicts the QoS of the unused Web services from other consumers’ experiences. Experimental results show that with this approach the preciseness of QoS prediction for Web services can be improved significantly.