Zibin Zheng

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.

WSRec: A Collaborative Filtering Based Web Service Recommender System

As the abundance of Web services on the World Wide Web increase,designing effective approaches for Web service selection and recommendation has become more and more important. In this paper, we present WSRec, a Web service recommender system, to attack this crucial problem. WSRec includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web service QoS value prediction. WSRec is implemented by Java language and deployed to the real-world environment. To study the prediction performance, A total of 21,197 public Web services are obtained from the Internet and a large-scale real-world experiment is conducted, where more than 1.5 millions test results are collected from 150 service users in different countries on 100 publicly available Web services located all over the world. The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches.

Distributed QoS Evaluation for Real-World Web Services

Quality-of-Service (QoS) is widely employed for describing non-functional characteristics of Web services. Although QoS of Web services has been investigated in a lot of previous works, there is a lack of real-world Web service QoS datasets for validating new QoS based techniques and models of Web services. To study the performance of real-world Web services as well as provide reusable research datasets for promoting the research of QoS-driven Web services, we conduct several large-scale evaluations on real-world Web services. Firstly, addresses of 21,358 Web services are obtained from the Internet. Then, invocation failure probability performance of 150 Web services is assessed by 100 distributed service users. After that, response time and throughput performance of 5,825 Web services are evaluated by 339 distributed service users. Detailed experimental results are presented in this paper and comprehensive Web service QoS datasets are publicly released for future research.

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.

QoS Ranking Prediction for Cloud Services

Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.

Investigating QoS of Real-World Web Services

Quality of service (QoS) is widely employed for describing nonfunctional characteristics of web services. Although QoS of web services has been investigated intensively in the field of service computing, there is a lack of real-world web service QoS data sets for validating various QoS-based techniques and models. To investigate QoS of real-world web services and to provide reusable research data sets for future research, we conduct several large-scale evaluations on real-world web services. First, addresses of 21,358 web services are obtained from the Internet. Then, three large-scale real-world evaluations are conducted. In our evaluations, more than 30 million real-world web service invocations are conducted on web services in more than 80 countries by users from more than 30 counties. Detailed evaluation results are presented in this paper and comprehensive web service QoS data sets are publicly released online.

WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services

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.

WSExpress: A QoS-aware Search Engine for Web Services

Web services are becoming prevalent nowadays. Finding desired Web services is becoming an emergent and challenging research problem. In this paper, we present WSExpress (Web Service Express), a novel Web service search engine to expressively find expected Web services. WSExpress ranks the publicly available Web services not only by functional similarities to users’ queries, but also by nonfunctional QoS characteristics of Web services. WSExpress provides three searching styles, which can adapt to the scenario of finding an appropriate Web service and the scenario of automatically replacing a failed Web service with a suitable one. WSExpress is implemented by Java language and large-scale experiments employing real-world Web services are conducted. Totally 3,738 Web services (15,811 operations) from 69 countries are involved in our experiments. The experimental results show that our search engine can find Web services with the desired functional and non-functional requirements. Extensive experimental studies are also conducted on a well known benchmark dataset consisting of 1,000 Web service operations to show the recall and precision performance of our search engine.

Service-Generated Big Data and Big Data-as-a-Service: An Overview

With the prevalence of service computing and cloud computing, more and more services are emerging on the Internet, generating huge volume of data, such as trace logs, QoS information, service relationship, etc. The overwhelming service-generated data become too large and complex to be effectively processed by traditional approaches. How to store, manage, and create values from the service-oriented big data become an important research problem. On the other hand, with the increasingly large amount of data, a single infrastructure which provides common functionality for managing and analyzing different types of service-generated big data is urgently required. To address this challenge, this paper provides an overview of service-generated big data and Big Data-as-a-Service. First, three types of service-generated big data are exploited to enhance system performance. Then, Big Data-as-a-Service, including Big Data Infrastructure-as-a-Service, Big Data Platform-as-a-Service, and Big Data Analytics Software-as-a-Service, is employed to provide common big data related services (e.g., accessing service-generated big data and data analytics results) to users to enhance efficiency and reduce cost.

Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering

Quality-of-service-based (QoS) service selection is an important issue of service-oriented computing. A common premise of previous research is that the QoS values of services to target users are supposed to be all known. However, many of QoS values are unknown in reality. This paper presents a neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection. Compared with existing methods, the proposed method has three new features: 1) the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; 2) a data smoothing process to improve prediction accuracy; and 3) a similarity fusion approach to handle the data sparsity problem. In addition, a two-phase neighbor selection strategy is proposed to improve its scalability. An extensive performance study based on a public data set demonstrates its effectiveness.