WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services
ABSTRACT The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS prediction framework, called WSPred, to provide time-aware personalized QoS value prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
- SourceAvailable from: Yutao Ma
[Show abstract] [Hide abstract]
- "In recent literature of this topic, a few researchers attempted to incorporate the context including geographical location information  and invocation time information  into neighbor-based CF methods, and the leading advantages of these approaches about recommendation performance were validated by large-scale experiments on real-world Web service QoS data sets. Unlike simple and effective memory-based CF methods, the model-based CF approaches introduce data mining, machine learning techniques to find patterns or train a prediction model based on training data. "
ABSTRACT: Nowadays, more and more service consumers pay great attention to QoS (Quality of Service) when they find and select appropriate Web services. For most of the approaches to QoS-aware Web service recommendation, the list of Web services recommended to target users is generally obtained based on rating-oriented predictions, aiming at predicting the potential ratings that a target user may assign to the unrated services as accurately as possible. However, in some scenarios, high accuracy of rating predictions may not necessarily lead to satisfactory recommendation results. In this paper, we propose a ranking-oriented hybrid approach by combining item-based collaborative filtering techniques and latent factor models to address the problem of Web services ranking. In particular, the similarity between two Web services is measured in terms of the correlation coefficient between their rankings instead of between their ratings. Comprehensive experiments on the QoS data set composed of real-world Web services are conducted to test our approach, and the experimental results demonstrate that our approach outperforms other competing approaches.the 12th International Conference on Services Computing, New York City, USA; 06/2015
02/2015; 7(2):8-15. DOI:10.5815/ijmecs.2015.02.02
- "The second dataset ( and ) contains real-world QoS evaluation results from 339 users on 5,825 WSs. The third dataset  contains real-world QoS evaluation results from 142 users invoking 4,532WSs over 64 different time slots. "
[Show abstract] [Hide abstract]
- "A Hidden Markov Model (HMM) and queuing models are considered in resource planning to predict QoS performance in . A personalized Web service prediction framework is proposed in . Although a Web service QoS prediction model is proposed using the multidimensional QoS models in , the correlations among different dimensions are not considered. "
ABSTRACT: We propose a cloud service composition framework that selects the optimal composition based on an end user’s long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers’ historical QoS data and short-term advertisements represented using Time Series. The quality of the QoS prediction is improved by incorporating QoS attributes’ intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series’ inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.IEEE Transactions on Services Computing 01/2014; DOI:10.1109/TSC.2014.2373366 · 3.05 Impact Factor