Product Description
by Yilei Zhang (Author), Michael R. Lyu (Author)
1. Introduction.- 2. Neighborhood-Based QoS Prediction.- 3. Time-Aware Model-Based QoS Prediction.- 4. Online QoS Prediction.- 5. QoS-AwareWeb Service Searching.- 6. QoS-Aware Byzantine Fault Tolerance.- 7. Conclusion and Discussion.
Back Jacket
This book offers a systematic and practical overview of Quality of Service prediction in cloud and service computing. Intended to thoroughly prepare the reader for research in cloud performance, the book first identifies common problems in QoS prediction and proposes three QoS prediction models to address them. Then it demonstrates the benefits of QoS prediction in two QoS-aware research areas. Lastly, it collects large-scale real-world temporal QoS data and publicly releases the datasets, making it a valuable resource for the research community. The book will appeal to professionals involved in cloud computing and graduate students working on QoS-related problems.
Author Biography
Yilei Zhang received his PhD in Computer Science from the Chinese University of Hong Kong. His industry-specific experience in cloud and big data spans several years as an IT professional. His research interests include big data, service computing and cloud computing. He has served as a reviewer for a number of international journals as well as conferences including TSE, TR, TSC, WWW, WSDM, KDD, ISSRE, etc. He received the best student paper award at the ICWS 2010.
Michael R. Lyu received his PhD in Computer Science from the University of California, Los Angeles. He is currently a Professor at the Chinese University of Hong Kong's Computer Science and Engineering Department. He has published 450 peer-reviewed journal and conference papers. His research interests include software reliability engineering, distributed systems, fault-tolerant computing, service computing, multimedia information retrieval, and machine learning. He was named as the IEEE Reliability Society Engineer of the Year in 2010. He is a fellow of the IEEE, ACM and AAAS.
