Service Quality Of Service Information Research Articles

Neighborhood-aware web service quality prediction using deep learning

With the rapid growth of web services on the Internet, it becomes more difficult for users who want to choose the high-quality web services from a large number of functionally equivalent candidate services. Therefore, the prediction of quality of service (QoS) values according to the history of web services has received extensive attention. In recent years, deep learning has achieved great success in speech recognition, image processing, and natural language understanding. However, it is rarely applied to the service recommendation field. Therefore, a novel approach for QoS prediction named NDL (neighborhood-aware deep learning) is proposed. NDL first gets the Top-k neighbors of the user and the service through the Pearson correlation coefficient according to the service QoS information. Then, it extracts the potential features of the user neighbor and the service neighbor; after that, it inputs the QoS values of the user and the user neighbor as well as the QoS values of the service and service neighbors as a convolutional neural network. The results of experiments conducted on a real-world dataset demonstrate that the NDL significantly outperforms the current QoS prediction method in prediction accuracy.

Privacy preserving service selection using fully homomorphic encryption scheme on untrusted cloud service platform

In this paper, we present a privacy-preserving service selection framework for cloud-based service systems. In the cloud-based service system, a cloud provider selects the best service from a set of services based on their Quality-of-Service (QoS) information. The QoS information of services is sensitive from the service provider’s point of view. We claim that the service selection process in the cloud can be biased. A service provider can bribe a dishonest employee of the cloud provider for taking unfair advantage during a service selection process. Therefore, it is important to execute the service selection tasks keeping QoS information private. We use a fully homomorphic encryption (FHE) scheme in this paper for encrypting QoS values. Service selection task is performed by the cloud provider on encrypted QoS values to ensure privacy. In order to reduce computation overhead, we propose a MapReduce model for parallel execution. We conduct several experiments to evaluate the performance of our proposed privacy preserving service selection framework using synthetic QoS dataset.

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.

A Collaborative QoS-Aware Service Evaluation Method for Service Selection

With the increasing deployment of functionally similar web services by different service providers, non-functional aspects, which are often referred to Quality of Service (QoS), play a vital role in ranking web services. Various users may have different priorities of QoS attributes. However, ordinary users are lacking in the ability to map their plain requirements to the obscure technical QoS metrics advertised by service providers. Moreover, the QoS information advertised by different service providers is untrustworthy due to various factors. Even after a painstaking analysis of the candidate services, users will still have a difficult time in the selection of services. To address the issue of web service selection, we propose in this paper a collaborative QoS-aware service evaluation method which is based on the Analytical Hierarchy Process (AHP) theory. In our method, QoS data of services is recorded by past users according to their usage experiences. A potential user collects QoS information of services from past users. The service with the highest benefit to cost ratio among candidate services is selected through the aggregation of the collected QoS information. Simulation results are provided to illustrate the performance of our proposal.

QoS-Aware Web Service Recommendation by Collaborative Filtering

With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.

An Extensible Management Framework for Web Service QoS

Web service is emerging as a promising technology and attracts many attentions from industry and research domains. With the popularity of service technology, many researchers propose that not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration. Researches have intensively explored many QoS related problems, such as QoS-aware dynamic service selection and composition, etc. However, existing approaches emphasize on how to make use of QoS information, the technologies on collecting, storing, measuring Web service QoS information are not intensively explored. It’s obvious that these fundamental technologies are also very important for successfully building QoS related applications. To address this problem, this paper proposes an extensible QoS information management framework, analyzes the technologies included in this framework, and presents the design decisions and solutions in Software Component Library Project of Peking University. Finally, this paper describes a real case study of applying this management framework in a project of 863 Program. The case study shows that users can extend the management framework and manage Web Service QoS according to their domain-specific requirements, which evaluates the extensibility and practicability of this QoS information management framework.