Temporal QoS management in scientific 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 so...
| Autor principal: | |
|---|---|
| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Waltham, Mass. :
Elsevier Science
2012.
|
| Edición: | 1st edition |
| Colección: | Elsevier insights.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628287806719 |
Tabla de Contenidos:
- Front Cover; Temporal QoS Management in Scientific Cloud Workflow Systems; Copyright Page; Contents; Preface; Acknowledgements; About the Authors; 1 Introduction; 1.1 Temporal QoS in Scientific Cloud Workflow Systems; 1.2 Motivating Example and Problem Analysis; 1.2.1 Motivating Example; 1.2.2 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 System4 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; 5 Forecasting Scientific Cloud Workflow Activity Duration Intervals; 5.1 Cloud Workflow Activity Durations; 5.2 Related Work and Problem Analysis; 5.2.1 Related Work; 5.2.2 Problem Analysis; 5.3 Statistical Time-Series-Pattern-Based Forecasting Strategy; 5.3.1 Statistical Time-Series Patterns; 5.3.2 Strategy Overview
- 5.3.3 Novel Time-Series Segmentation Algorithm: K-MaxSDev5.3.4 Forecasting Algorithms; 5.4 Evaluation; 5.4.1 Example Forecasting Process; 5.4.2 Comparison Results; 6 Temporal Constraint Setting; 6.1 Related Work and Problem Analysis; 6.1.1 Related Work; 6.1.2 Problem Analysis; 6.2 Probability-based Temporal Consistency Model; 6.2.1 Weighted Joint Normal Distribution for Workflow Activity Durations; 6.2.2 Probability-based Temporal Consistency Model; 6.3 Setting Temporal Constraints; 6.3.1 Calculating Weighted Joint Distribution; 6.3.2 Setting Coarse-grained Temporal Constraints
- 6.3.3 Setting Fine-grained Temporal Constraints6.4 Case Study; 7 Temporal Checkpoint Selection and Temporal Verification; 7.1 Related Work and Problem Analysis; 7.1.1 Related Work; 7.1.2 Problem Analysis; 7.2 Temporal Checkpoint Selection and Verification Strategy; 7.2.1 Probability Range for Statistically Recoverable Temporal Violations with Light-Weight Temporal Violation Handling Strategies; 7.2.2 Minimum Probability Time Redundancy; 7.2.3 Temporal Checkpoint Selection and Temporal Verification Process; 7.3 Evaluation; 7.3.1 Experimental Settings; 7.3.2 Experimental Results
- 8 Temporal Violation Handling Point Selection8.1 Related Work and Problem Analysis; 8.1.1 Related Work; 8.1.2 Problem Analysis; 8.2 Adaptive Temporal Violation Handling Point Selection Strategy; 8.2.1 Probability of Self-Recovery; 8.2.2 Temporal Violation Handling Point Selection Strategy; Temporal Violation Handling Point Selection Rule; Adaptive Modification Process for Probability Threshold; 8.3 Evaluation; 9 Temporal Violation Handling; 9.1 Related Work and Problem Analysis; 9.1.1 Related Work; 9.1.2 Problem Analysis; 9.2 Overview of Temporal Violation Handling Strategies
- 9.2.1 Temporal Violation Handling of Statistically Recoverable Temporal Violations
