QCI-WSC: Estimation and prediction of QoS confidence interval for web service composition based on Bootstrap

Two-stage approach for reliable dynamic Web service composition

Quality of Service (QoS) has been widely used for personalized Web service recommendation. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a challenge of Web service recommendation. Time series forecasting and collaborative filtering are two mainstream technologies for QoS prediction. However, on one hand, existing time series forecasting approaches based on Auto Regressive Integrated Moving Average (ARIMA) models do not take the latest observation as a feedback to revise forecasts. Moreover, they only focus on predicting future QoS values for each individual Web service. Service users' personalized factors are not taken into account. On the other hand, collaborative filtering facilitates user-side personalized QoS evaluation, but rarely precisely models the temporal dynamics of QoS values. To address the limitations of existing QoS prediction methods, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the personalized factors of service users. Our approach seamlessly combines collaborative filtering with improved time series forecasting which uses Kalman filtering to compensate for shortcomings of ARIMA models. Finally, the experimental results show that the proposed approach can improve the accuracy of personalized QoS prediction significantly.

A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm

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.

Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.

With the incessant growth of web services on the Internet, how to design effective web service recommendation technologies based on Quality of Service (QoS) is becoming more and more important. Web service recommendation can relieve users from tough work on service selection and improve the efficiency of developing service-oriented applications. Neighborhood-based collaborative filtering has been widely used for web service recommendation, in which similarity measurement and QoS prediction are two key issues. However, traditional similarity models and QoS prediction methods rarely consider the influence of time information, which is an important factor affecting the QoS performance of web services. Furthermore, it is difficult for the existing similarity models to capture the actual relationships between users or services due to data sparsity. The two shortcomings seriously devalue the performance of neighborhood-based collaborative filtering. In this paper, the authors propose an improved time-aware collaborative filtering approach for high-quality web service recommendation. Our approach integrates time information into both similarity measurement and QoS prediction. Additionally, in order to alleviate the data sparsity problem, a hybrid personalized random walk algorithm is designed to infer indirect user similarities and service similarities. Finally, a series of experiments are provided to validate the effectiveness of our approach.

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.

In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

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

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.

With the incessant growth of Web services on the Internet, designing effective Web service recommendation technologies based on Quality of Service (QoS) is becoming more and more important. Neighborhood-based Collaborative Filtering has been widely used for Web service recommendation, in which similarity measurement and QoS prediction are two key steps. However, traditional similarity models and QoS prediction methods rarely consider the influence of time information, which is an important factor affecting the QoS of Web services. Furthermore, traditional similarity models fail to capture the actual relationships between users or services due to data sparsity. These shortcomings seriously devalue the performance of neighborhood-based Collaborative Filtering. In order to make high-quality Web service recommendation, we propose a novel time-aware approach, which integrates time information into both the similarity measurement and the final QoS prediction. Additionally, in order to alleviate the data sparsity problem, a hybrid personalized random walk algorithm is employed to infer more indirect user similarities and service similarities. Finally, we conduct series of experiments to validate the effectiveness of our approaches.

The spread over of huge amount of information in the internet makes it really difficult for the users to obtain the relevant search items. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfil the need of internet user, there is a need to know their preferences of search at various contexts. Hence, it is preferred to select the web service with best quality of service (QoS) performance to satisfy the needs of user. This paper presents a location-aware collaborative filtering (CF) and association-based clustering approach for web service recommendation. The similarity between users and web services is measured by considering the personalised deviation of QoS of web services and QoS experiences of users. Hence, web service recommendation becomes a really challenging and time-consuming task due to the large search space. To reduce the search space, clustering of the web services into clusters is an efficient approach. The services are clustered based on the semantic similarity and association between them. Our proposed approach recommends services using the generated clusters and services with better QoS values.

Quality of Service (QoS) has been widely used to support dynamic Web Service (WS) selection and composition. Due to the volatile nature of QoS parameters, QoS prediction has been put forward to understand the trend of QoS data volatility and estimate QoS values in dynamic environments. In order to provide adaptive and effective QoS prediction, we propose a WS QoS prediction approach, named WS-QoSP, based on the technique of forecast combination. Different from the existing QoS prediction approaches that choose a most feasible forecasting model and predict relying on this "best" model, WS-QoSP selects multiple potential forecasting models, combines the results of the selected models to optimize the overall forecast accuracy. Results of real data experiments demonstrate the diversified forecast accuracy gains by using WS-QoSP.

The recent trend in the web world is to accomplish almost all the user services in every field through the web portals of the respective organizations. But a specific task with series of actions cannot be completed by a single web service with limited functionality. Therefore, multiple web services with different functionalities are composed together to attain the result. Web service composition is an approach that combine various services to fulfill the Web related tasks with preferred quality. Composition of such services will become more challenging when these web services are with similar functionalities, varying Quality and from several providers. Hence, the overall QoS (Quality of Service) could be considered as the major factor for composition. Moreover, in most of the compositions the expected QoS cannot be attained when the task is finished. Sometimes the complete task may have affected by a poor performed single web service. So, while composition, at most care should be taken to select a particular web service. Composing web services dynamically is the main method used to overcome these difficulties. However, to reach the actual functionality of the specific task the quality of each individual service is very much necessary. The QoS of a web service normally evaluated using the non-functional attributes, such as response time, availability, reliability, throughput, etc. Also, while composition, the same level of quality is not expected for individual web services that are included in the chain. So, a framework proposed in this research paper, for web service composition by setting appropriate weightage for the non-functional parameters. Experimental results show that implementation of this method will definitely pave the way to reach the maximum performance of the composition with improved QoS.

The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.