Temporal QOS Management in Scientific Cloud Workflow Systems

Xiao Liu, Swinburne University of Technology, Melbourne, Australia
Jinjun Chen, University of Technology, Sydney, Australia
Yun Yang, Swinburne University of Technology, Melbourne, Australia

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.

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Paperback, 154 Pages

Published: February 2012

Imprint: Elsevier

ISBN: 978-0-12-397010-7

Chapter 1 Introduction
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
Chapter 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
Chapter 3 A Scientific Cloud Workflow System
Chapter 4 Novel Probabilistic Temporal Framework
4.1 Framework Overview
4.2 Component 1: Temporal Constraint Setting
4.3 Component 2: Temporal Consistency Monitoring
4.4 Component 3: Temporal Violation Handling
Chapter 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
5.4 Evaluation
Chapter 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
Chapter 7 Temporal Checkpoint Selection and Temporal Verification
7.1 Related Work and Problem Analysis
7.2 Temporal Checkpoint Selection and Verification Strategy
7.3 Evaluation
Chapter 8 Temporal Violation Handling Point Selection
8.1 Related Work and Problem Analysis
8.2 Adaptive Temporal Violation Handling Point Selection Strategy
8.3 Evaluation
Chapter 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
Chapter 10 Conclusions and Contribution
10.1 Overall Cost Analysis for Temporal Framework
10.2 Summary of This Book
10.3 Contributions of This Book
Bibliography
Appendix: Notation Index

Temporal QOS Management in Scientific Cloud Workflow Systems

Contents