Temporal QOS Management in Scientific Cloud Workflow Systems

By

  • 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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Audience

Researchers, practitioners, and graduate students in scientific computing.

 

Book information

  • Published: February 2012
  • Imprint: ELSEVIER
  • ISBN: 978-0-12-397010-7


Table of Contents

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