Temporal QOS Management in Scientific Cloud Workflow Systems

Informationen zum Autor Xiao Liu received his PhD degree in Computer Science and Software Engineering from the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia in 2011. He received his Master and Bachelor degree from the School of Management, Hefei University of Technology, Hefei, China, in 2007 and 2004 respectively, all in Information Management and Information Systems. He is currently a postdoctoral research fellow in the Centre of Computing and Engineering Software System at Swinburne University of Technology. His research interests include workflow management systems, scientific workflows, cloud computing, business process management and quality of service. Jinjun Chen received his PhD degree in Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2007. He is currently an Associate Professor in the Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include Scientific workflow management and applications, workflow management and applications in Web service or SOC environments, workflow management and applications in grid (service)/cloud computing environments, software verification and validation in workflow systems, QoS and resource scheduling in distributed computing systems such as cloud computing, service oriented computing, semantics and knowledge management, cloud computing. Yun Yang is currently a full professor in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne in 1999 as an associate professor, he was a lecturer and senior lecturer at Deakin University, Australia, during 1996-1999. He has coauthored four books and published over 200 papers in journals and refereed conference proceedings. He is currently on the Editorial Board of IEEE Transactions on Cloud Computing. His current research interests include software technologies, cloud computing, p2p/grid/cloud workflow systems, and service-oriented computing. Klappentext Cloud computing can provide virtually unlimited scalable high-performance computing resources. Cloud workflows often underlie many large-scale data/computation intensive e-science applications such as earthquake modeling! weather forecasting and astrophysics. During application modeling! these sophisticated processes are redesigned as cloud workflows! and at runtime! the models are executed by employing the supercomputing and data sharing ability of the underlying cloud computing infrastructures. Temporal QOS Management in Scientific Cloud Workflow Systems focuses on real-world scientific applications! which often must be completed by satisfying a set of temporal constraints such as milestones and deadlines. Meanwhile! activity duration as a measurement of system performance often needs to be monitored and controlled. This book demonstrates how to guarantee on-time completion of most! if not all! workflow applications. Offering a comprehensive framework to support the lifecycle of time-constrained workflow applications! this book will enhance the overall performance and usability of scientific cloud workflow systems. Zusammenfassung Offering a comprehensive framework to support the lifecycle of time-constrained workflow applications! this book enhances the overall performance and usability of scientific cloud workflow systems. It explains how to reduce the cost to detect and handle temporal violations while delivering high quality of service (QoS). ...

Autorentext
Xiao Liu received his PhD degree in Computer Science and Software Engineering from the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia in 2011. He received his Master and Bachelor degree from the School of Management, Hefei University of Technology, Hefei, China, in 2007 and 2004 respectively, all in Information Management and Information Systems. He is currently a postdoctoral research fellow in the Centre of Computing and Engineering Software System at Swinburne University of Technology. His research interests include workflow management systems, scientific workflows, cloud computing, business process management and quality of service. Jinjun Chen received his PhD degree in Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2007. He is currently an Associate Professor in the Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include Scientific workflow management and applications, workflow management and applications in Web service or SOC environments, workflow management and applications in grid (service)/cloud computing environments, software verification and validation in workflow systems, QoS and resource scheduling in distributed computing systems such as cloud computing, service oriented computing, semantics and knowledge management, cloud computing.Yun Yang is currently a full professor in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne in 1999 as an associate professor, he was a lecturer and senior lecturer at Deakin University, Australia, during 1996-1999. He has coauthored four books and published over 200 papers in journals and refereed conference proceedings. He is currently on the Editorial Board of IEEE Transactions on Cloud Computing. His current research interests include software technologies, cloud computing, p2p/grid/cloud workflow systems, and service-oriented computing.

Klappentext
Offering a comprehensive framework to support the lifecycle of time-constrained workflow applications, this book enhances the overall performance and usability of scientific cloud workflow systems. It explains how to reduce the cost to detect and handle temporal violations while delivering high quality of service (QoS).

Inhalt

1. Introduction 2. Literature Review and Problem Analysis 3. A Scientific Cloud Workflow System 4. Novel Probabilistic Temporal Framework 5. Forecasting Scientific Cloud Workflow Activity Duration Intervals 6. Temporal Constraint Setting 7. Temporal Checkpoint Selection and Temporal Verification 8. Temporal Violation Handling Point Selection 9. Temporal Violation Handling 10. Conclusions and Contribution Bibliography Appendix: Notation Index