Web Service Quality Of Service Prediction Research Articles

Time-Aware Smart City Services Based on QoS Prediction: A Contrastive Learning Approach

Smart cities are designed to satisfy the needs of residents and improve their quality of life by providing a wide range of smart city services. The key to the efficient operation of smart city services is accurate forecast of the missing QoS (Quality of Service).. At present, many approaches utilize the context information of users and services, such as geographic location and network location, to somewhat increase the prediction accuracy and forecast the missing QoS values.. However, because the network conditions and server status are unpredictable, time is also considered as one of the important factors affecting QoS prediction, which brings more challenges as follows: higher data dimension, more complex data characteristics and higher data sparsity. To overcome these challenges,z we suggest an approach for time-aware Web service QoS prediction based on contrastive learning(named CLpred). CLpred utilizes a sequential data input format for QoS data and models these QoS sequences through Transformer encoder with contrastive learning framework. Therefore, it can downscale QoS data and extract a more efficient representation in complex QoS data. Furthermore, it makes it possible to apply data augmentation methods to address these probelms of data sparsity. In order to prove the superiority of the this approach, particularly inside the presence of extremely high data sparsity, extensive tests are carried out on the well-known service QoS data set WSDREAM.

Web Service QoS Prediction via Collaborative Filtering: A Survey

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

A Location-Based Factorization Machine Model for Web Service QoS Prediction

With the prevalence of web services, a large number of similar web services are provided by different providers. To select the optimal service among these service candidates, Quality of Service (QoS), representing the non-functional characteristics, plays an important role. To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. Collaborative web service QoS prediction is one of the most popular approaches. Based on the historical QoS data, collaborative QoS prediction methods employ memory-based collaborative filtering (CF), model-based CF, or their hybrids to predict QoS values. However, these methods usually only consider the QoS information of similar users and services, neglecting the correlation between them. To enhance the prediction accuracy, we propose a novel method to predict QoS values based on factorization machine, which leverages not only QoS information of users and services but also the user and service neighbor’s information. To evaluate our approach, we conduct experiments on a large-scale real-world dataset with 1,974,675 web service invocations. The experiment results show that our approach achieves higher prediction accuracy than other QoS prediction methods.

An accurate and efficient web service QoS prediction model with wide-range awareness

With the wide spread and deepening of service-oriented computing, more and more enterprises and organizations are constructing their applications by integrating third-party Web services in the cloud nowadays. Building high-quality applications has long been a critical research issue. Quality of service (QoS) prediction provides valuable information for making optimal Web service selection from a set of functionally equivalent candidate services. Commonly, collaborative filtering technique like matrix factorization (MF) is implemented for predicting unknown QoS values, and most of them are built via modeling user-service interaction based on QoS data directly or take side information such as geographical location, network autonomous region into account. Due to the overlook of the implicit but important wide-range characteristic of QoS data, existing MF methods might incur high users’ and services’ biases, and their prediction accuracy will not be good enough if we are faced with such wide-range of QoS data. In this work, we first investigate the wide-range characteristic among users and services via real-world Web service QoS dataset, and argue that such observed finding is an essential factor for accurate QoS prediction. We then propose a novel prediction model named Wide-Range Aware Matrix Factorization (WRAMF), which tackles the wide-range influence via bias information combination and an active function mapping explicitly. The proposed WRAMF model is advantageous to existing MF-based models, which optimizes the model by an adaptive learning rate strategy that guides WRAMF to approach the optimal solution accurately, and trains the model by a well-designed parallel stochastic gradient descent algorithm efficiently. Comprehensive experiments are conducted by employing real-world QoS dataset and empirical results show that our WRAMF significantly outperforms the state-of-the-art methods in terms of accuracy and efficiency.

Web service QoS prediction: when collaborative filtering meets data fluctuating in big-range

Service recommendation aims to help users to find the most suitable Web services based on their quality of service (QoS) preferences instead of searching through extensive volume of Web services using search engine manually. Accurate unknown QoS rating prediction is one of the key challenges in the analysis of service recommendation. Collaborative filtering (CF) is a well-known recommendation method that estimates missing ratings by employing a set of similar users to the active user. The core idea of CF consists of picking out an appropriate set of users and using them in the rating prediction process. However, the majority of existing CF methods are not well-designed for Web service QoS prediction as they ignore the implicit but important characteristic of Web service QoS data that fluctuate in big-range. In other words, through analysis of real-world QoS datasets, we observed that QoS ratings vary widely and they are highly skewed with large variances, as two main facts, which dramatically degrade the accuracy of CF methods in QoS prediction. Towards this problem, in this paper, we propose a big-range aware collaborative filtering approach dubbed BRACF to predict Web service QoS ratings accurately. Specifically, since big-range of QoS data can lead to similarity exaggeration, we design a simple yet effective similarity model which considers the influence of big-range among users’ QoS data for accurately characterizing the similarity between users. Moreover, the similarity model is seamlessly incorporated into CF model for identifying similar neighbor using Top-K strategy and then it generates QoS predictions by combining bias information. Through extensive experiments on two public real-world Web service QoS for datasets, as response time and throughput, we show that BRACF significantly outperforms state-of-the-art CF methods. We believe that this work demonstrates the potential impact of big range data for the accurate QoS prediction.

A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction

Neighborhood regularization is highly important for a latent factor (LF)-based Quality-of-Service (QoS)-predictor since similar users usually experience similar QoS when invoking similar services. Current neighborhood-regularized LF models rely prior information on neighborhood obtained from common raw QoS data or geographical information. The former suffers from low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is usually difficult to collect considering information security, identity privacy, and commercial interests in real-world scenarios. To address the above issues, this work proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where the LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood of users/services and <inline-formula><tex-math notation="LaTeX">$L_{2}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="luo-ieq1-2961895.gif"/></alternatives></inline-formula>-norm of desired LFs. Experimental results from large scale QoS datasets demonstrate that PLF outperforms state-of-the-art models in terms of both accuracy and efficiency.

Your neighbors are misunderstood: On modeling accurate similarity driven by data range to collaborative web service QoS prediction

Quality of service (QoS) is a set of non-functional attributes of Web services for differentiating enriched Web services with same or similar functionality. Predicting the unknown QoS of Web services for service users is often required for any QoS based service computing because QoS plays a fundamental role in reliable Web service recommendation, composition and selection. Existing collaborative filtering based QoS prediction methods suffer from a serious acclimatization issue caused by the difference of QoS data range, which dramatically degrades the prediction accuracy and even impedes its adaptability. The fact that Web service QoS data exhibit large service effect with different data ranges, is verified on public real-world datasets. In this study, we aim to tackle the problem of QoS prediction while considering the influences of QoS data range in the context of collaborative filtering. In particular, a simple yet effective similarity model called JacMinMax, which is driven by QoS data range, is designed. Furthermore, two neighborhood selection strategies using JacMinMax are proposed, and the obtained neighbors are systemically integrated into neighborhood- and model-based methods for collaborative QoS predictions. Experimental results show that the proposed method efficiently alleviates the influence of the concerned QoS data ranges, and performs better than many state-of-the-art approaches with respect to accuracy.

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.

Location-aware and personalized collaborative filtering for web service recommendation

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

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.

Efficient web service QoS prediction using local neighborhood matrix factorization

In the era of Big Data, companies worldwide are actively deploying web services in both intranet and internet environments. Quality-of-Service (QoS), the fundamental aspect of web service has thus attracted numerous attention in industry and academia. The study on sufficient QoS data keeps advancing the state in Service-Oriented Computing (SOC) area. To collect a large amount of resource in practice, QoS prediction applications are designed and built. Nevertheless, how to generate accurate results in high productivity is still a main challenge to existing frameworks. In this paper, we propose LoNMF, a Local Neighborhood Matrix Factorization application that incorporates domain knowledge in modern Artificial Intelligence (AI) technique to tackle this challenge. LoNMF first proposes a two-level selection mechanism that can identify a set of highly relevant local neighbors for target user. And then, it integrates the geographical information to build up an extended Matrix Factorization (MF) approach for personalized QoS prediction. Finally, it iteratively generates results by utilizing hints from previous round computations, a gradient boosting strategy that directly accelerates solving process. Experimental evidence on large-scale real-world QoS data shows that LoNMF is scalable, and consistently outperforming other state-of-the-art applications in prediction accuracy and efficiency.

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 location-aware hybrid web service QoS prediction algorithm

Quality-of-service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. In this paper, a new hybrid user-location-aware prediction based on WAA (HUWAA) is proposed. The implicit neighbor search is optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem is validated in experiments on public real world datasets. The new algorithms outperform the existing IPCC, UPCC and WSRec algorithms.

Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

With the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users. We first apply the concept of user-collaboration for the web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 web service invocations from 339 service users on 5,825 real-world web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our web service QoS data set provides valuable real-world data for future research.