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El. knyga: Temporal QOS Management in Scientific Cloud Workflow Systems

(University of Technology, Sydney, Australia), (Swinburne University of Technology, Melbourne, Australia), (Swinburne University of Technology, Melbourne, Australia)
  • Formatas: PDF+DRM
  • Išleidimo metai: 20-Feb-2012
  • Leidėjas: Elsevier Science Publishing Co Inc
  • Kalba: eng
  • ISBN-13: 9780123972958
Kitos knygos pagal šią temą:
Temporal QOS Management in Scientific Cloud Workflow SystemsDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 20-Feb-2012
  • Leidėjas: Elsevier Science Publishing Co Inc
  • Kalba: eng
  • ISBN-13: 9780123972958
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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


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

Daugiau informacijos

Offers a comprehensive framework to support the lifecycle of time-constrained workflow applications
Preface ix
Acknowledgements xi
About the Authors xiii
1 Introduction
1(10)
1.1 Temporal QoS in Scientific Cloud Workflow Systems
1(2)
1.2 Motivating Example and Problem Analysis
3(4)
1.2.1 Motivating Example
3(2)
1.2.2 Problem Analysis
5(2)
1.3 Key Issues of This Research
7(1)
1.4 Overview of This Book
8(3)
2 Literature Review and Problem Analysis
11(6)
2.1 Workflow Temporal QoS
11(1)
2.2 Temporal Consistency Model
12(1)
2.3 Temporal Constraint Setting
13(1)
2.4 Temporal Consistency Monitoring
14(1)
2.5 Temporal Violation Handling
15(2)
3 A Scientific Cloud Workflow System
17(6)
4 Novel Probabilistic Temporal Framework
23(10)
4.1 Framework Overview
23(3)
4.2 Component I: Temporal Constraint Setting
26(2)
4.3 Component II: Temporal Consistency Monitoring
28(1)
4.4 Component III: Temporal Violation Handling
29(4)
5 Forecasting Scientific Cloud Workflow Activity Duration Intervals
33(22)
5.1 Cloud Workflow Activity Durations
33(2)
5.2 Related Work and Problem Analysis
35(2)
5.2.1 Related Work
35(1)
5.2.2 Problem Analysis
36(1)
5.3 Statistical Time-Series-Pattern-Based Forecasting Strategy
37(9)
5.3.1 Statistical Time-Series Patterns
38(1)
5.3.2 Strategy Overview
39(2)
5.3.3 Novel Time-Series Segmentation Algorithm: K-MaxSDev
41(2)
5.3.4 Forecasting Algorithms
43(3)
5.4 Evaluation
46(9)
5.4.1 Example Forecasting Process
46(4)
5.4.2 Comparison Results
50(5)
6 Temporal Constraint Setting
55(16)
6.1 Related Work and Problem Analysis
55(3)
6.1.1 Related Work
55(2)
6.1.2 Problem Analysis
57(1)
6.2 Probability-Based Temporal Consistency Model
58(6)
6.2.1 Weighted Joint Normal Distribution for Workflow Activity Durations
58(4)
6.2.2 Probability-Based Temporal Consistency Model
62(2)
6.3 Setting Temporal Constraints
64(4)
6.3.1 Calculating Weighted Joint Distribution
64(1)
6.3.2 Setting Coarse-grained Temporal Constraints
65(1)
6.3.3 Setting Fine-grained Temporal Constraints
66(2)
6.4 Case Study
68(3)
7 Temporal Checkpoint Selection and Temporal Verification
71(10)
7.1 Related Work and Problem Analysis
72(1)
7.1.1 Related Work
72(1)
7.1.2 Problem Analysis
72(1)
7.2 Temporal Checkpoint Selection and Verification Strategy
73(3)
7.2.1 Probability Range for Statistically Recoverable Temporal Violations with Light-Weight Temporal Violation Handling Strategies
73(1)
7.2.2 Minimum Probability Time Redundancy
74(1)
7.2.3 Temporal Checkpoint Selection and Temporal Verification Process
75(1)
7.3 Evaluation
76(5)
7.3.1 Experimental Settings
76(2)
7.3.2 Experimental Results
78(3)
8 Temporal Violation Handling Point Selection
81(14)
8.1 Related Work and Problem Analysis
81(2)
8.1.1 Related Work
81(1)
8.1.2 Problem Analysis
82(1)
8.2 Adaptive Temporal Violation Handling Point Selection Strategy
83(2)
8.2.1 Probability of Self-Recovery
83(1)
8.2.2 Temporal Violation Handling Point Selection Strategy
84(1)
8.3 Evaluation
85(10)
9 Temporal Violation Handling
95(32)
9.1 Related Work and Problem Analysis
95(2)
9.1.1 Related Work
95(2)
9.1.2 Problem Analysis
97(1)
9.2 Overview of Temporal Violation Handling Strategies
97(3)
9.2.1 Temporal Violation Handling of Statistically Recoverable Temporal Violations
98(1)
9.2.2 Temporal Violation Handling of Statistically Non-Recoverable Temporal Violations
99(1)
9.3 A Novel General Two-Stage Local Workflow Rescheduling Strategy for Recoverable Temporal Violations
100(7)
9.3.1 Description of the General Strategy
100(3)
9.3.2 Metaheuristic Algorithm 1: GA
103(2)
9.3.3 Metaheuristic Algorithm 2: ACO
105(2)
9.3.4 Other Representative Metaheuristic Algorithms
107(1)
9.4 Three-Level Temporal Violation Handling Strategy
107(5)
9.4.1 PTDA for Level I Violations
109(1)
9.4.2 ACOWR for Level II Violations
110(1)
9.4.3 PTDA + ACOWR for Level III Violations
110(2)
9.5 Comparison of GA- and ACO-based Workflow Rescheduling Strategies
112(10)
9.5.1 Experimental Settings
112(3)
9.5.2 Experimental Results
115(7)
9.6 Evaluation of Three-Level Temporal Violation Handling Strategy
122(5)
9.6.1 Violation Rates of Local and Global Temporal Constraints
122(2)
9.6.2 Cost Analysis for a Three-Level Handling Strategy
124(3)
10 Conclusions and Contribution
127(6)
10.1 Overall Cost Analysis for Temporal Framework
127(1)
10.2 Summary of This Book
128(2)
10.3 Contributions of This Book
130(3)
Appendix: Notation Index 133(2)
Bibliography 135
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.
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