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 modelling, weather forecasting and astrophysics. During application modelling, 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. - Explains how to reduce the cost to detect and handle temporal violations while delivering high quality of service (QoS) - Offers new concepts, innovative strategies and algorithms to support large-scale sophisticated applications in the cloud - Improves the overall performance and usability of cloud workflow systems
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.
Inhalt
Chapter 1 Introduction
Chapter 2 Literature Review and Problem Analysis
Chapter 3 A Scientific Cloud Workflow System
Chapter 4 Novel Probabilistic Temporal Framework
Chapter 5 Forecasting Scientific Cloud Workflow Activity Duration Intervals
Chapter 6 Temporal Constraint Setting
Chapter 7 Temporal Checkpoint Selection and Temporal Verification
Chapter 8 Temporal Violation Handling Point Selection
Chapter 9 Temporal Violation Handling
Chapter 10 Conclusions and Contribution
Bibliography
Appendix: Notation Index