Yilei Zhang

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.

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.

Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing

With the increasing popularity of cloud computing as a solution for building high-quality applications on distributed components, efficiently evaluating user-side quality of cloud components becomes an urgent and crucial research problem. However, invoking all the available cloud components from user-side for evaluation purpose is expensive and impractical. To address this critical challenge, we propose a neighborhood-based approach, called CloudPred, for collaborative and personalized quality prediction of cloud components. CloudPred is enhanced by feature modeling on both users and components. Our approach CloudPred requires no additional invocation of cloud components on behalf of the cloud application designers. The extensive experimental results show that CloudPred achieves higher QoS prediction accuracy than other competing methods. We also publicly release our large-scale QoS dataset for future related research in cloud computing.

CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing

The rising popularity of cloud computing makes building high quality cloud applications a critical and urgently required research problem. Component quality ranking approaches are crucial for making optimal component selection from a set of functionally equivalent component candidates. Moreover, quality ranking of cloud components helps the application designers detect the poor performing components in the complex cloud applications, which usually include huge number of distributed components. To provide personalized cloud component ranking for different designers of cloud applications, this paper proposes a QoS-driven component ranking framework for cloud applications by taking advantage of the past component usage experiences of different component users. Our approach requires no additional invocations of the cloud components on behalf of the application designers. The extensive experimental results show that our approach outperforms the competing approaches.

BFTCloud: A Byzantine Fault Tolerance Framework for Voluntary-Resource Cloud Computing

Cloud computing is becoming a popular and important solution for building highly reliable applications on distributed resources. However, it is a critical challenge to guarantee the system reliability of applications especially in voluntary-resource cloud due to the highly dynamic environment. In this paper, we present BFT Cloud (Byzantine Fault Tolerant Cloud), a Byzantine fault tolerance framework for building robust systems involuntary-resource cloud environments. BFT Cloud guarantees robustness of systems when up to f of totally 3f+1 resource providers are faulty, including crash faults, arbitrary behaviors faults, etc. BFT Cloud is evaluated in a large-scale real-world experiment which consists of 257 voluntary-resource providers located in 26 countries. The experimental results shows that BFT Cloud guarantees high reliability of systems built on the top of voluntary-resource cloud infrastructure and ensures good performance of these systems.

An Online Performance Prediction Framework for Service-Oriented Systems

The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance prediction framework, called OPred, to provide personalized service-oriented system performance prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.

Real-Time Performance Prediction for Cloud Components

Cloud computing provides access to large pools of distributed components for building high-quality applications. User-side performance of cloud components highly depends on the remote server status as well as the unpredictability of the Internet, which are variable over time. It is an important task to explore an method to predict the real-time performance of cloud components. To address this critical challenge, this paper proposes a prediction framework to predict real-time component performance effectively. Our prediction framework builds feature models based on the past usage experience of different users and employs time series analysis techniques on feature trends to make performance prediction. The results of large-scale experiments show the effectiveness and efficiency of our method.