QoS Prediction in Cloud and Service Computing

This chapter provides an overview of QoS prediction in cloud and service computing, including backgrounds, related works, and organizations of this book.

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.

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 chapter 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.

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 runtime. To address this critical challenge, this chapter 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.

Web services are becoming prevalent nowadays. Finding desired Web services is becoming an emergent and challenging research problem. In this chapter, 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 user queries, but also by non-functional 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, 3738 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 1000 Web service operations to show the recall and precision performance of our search engine.

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 chapter, we present Byzantine fault-tolerant cloud (BFTCloud ), a Byzantine fault tolerance framework for building robust systems in voluntary-resource cloud environments. BFTCloud guarantees robustness of systems when up to f of totally 3f + 1 resource providers are faulty, including crash faults and arbitrary behaviors faults. BFTCloud is evaluated in a large-scale real-world experiment which consists of 257 voluntary-resource providers located in 26 countries. The experimental results show that BFTCloud guarantees high reliability of systems built on the top of voluntary-resource cloud infrastructure and ensure good performance of these systems.

This chapter concludes this book and discusses the future work.