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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
Researchers, practitioners, and graduate students in scientific computing.
About the Authors
1.1 Temporal QoS in Scientific Cloud Workflow Systems
1.2 Motivating Example and Problem Analysis
1.3 Key Issues of This Research
1.4 Overview of This Book
2. Literature Review and Problem Analysis
2.1 Workflow Temporal QoS
2.2 Temporal Consistency Model
2.3 Temporal Constraint Setting
2.4 Temporal Consistency Monitoring
2.5 Temporal Violation Handling
3. A Scientific Cloud Workflow System
4. Novel Probabilistic Temporal Framework
4.1 Framework Overview
4.2 Component I: Temporal Constraint Setting
4.3 Component II: Temporal Consistency Monitoring
4.4 Component III: Temporal Violation Handling
COMPONENT I. Temporal Constraint Setting
5. Forecasting Scientific Cloud Workflow Activity Duration Intervals
5.1 Cloud Workflow Activity Durations
5.2 Related Work and Problem Analysis
5.3 Statistical Time-Series-Pattern-Based Forecasting Strategy
6. Temporal Constraint Setting
6.1 Related Work and Problem Analysis
6.2 Probability-based Temporal Consistency Model
6.3 Setting Temporal Constraints
6.4 Case Study
COMPONENT II. Temporal Consistency Monitoring
7. Temporal Checkpoint Selection and Temporal Verification
7.1 Related Work and Problem Analysis
7.2 Temporal Checkpoint Selection and Verification Strategy
COMPONENT III. Temporal Violation Handling
8. Temporal Violation Handling Point Selection
8.1 Related Work and Problem Analysis
8.2 Adaptive Temporal Violation Handling Point Selection Strategy
9. Temporal Violation Handling
9.1 Related Work and Problem Analysis
9.2 Overview of Temporal Violation Handling Strategies
9.3 A Novel General Two-Stage Local Workflow Rescheduling Strategy for Recoverable Temporal Violations
9.4 Three-Level Temporal Violation Handling Strategy
9.5 Comparison of GA- and ACO-based Workflow Rescheduling Strategies
9.6 Evaluation of Three-Level Temporal Violation Handling Strategy
10. Conclusions and Contribution
10.1 Overall Cost Analysis for Temporal Framework
10.2 Summary of This Book
10.3 Contributions of This Book
APPENDIX. Notation Index
- No. of pages:
- © Elsevier 2012
- 20th February 2012
- Paperback ISBN:
- eBook ISBN:
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.
Swinburne University of Technology, Melbourne, Australia
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.
University of Technology, Sydney, Australia
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.
Swinburne University of Technology, Melbourne, Australia
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