
- 156 pages
- English
- PDF
- Available on iOS & Android
About this book
This book presents a system that learns new load indices and tunes the parameters of given migration policies. The key component is a dynamic workload generator that allows off-line measurement of task-completion times under a wide variety of precisely controlled loading conditions. The workload data collected are used for training comparator neural networks, a novel architecture for learning to compare functions of time series and for generating a load index to be used by the load balancing strategy. Finally, the load-index traces generated by the comparator networks are used in a population-based learning system for tuning the parameters of a given load-balancing policy. Together, the system constitutes an automated strategy-learning system for performance-driven improvement of existing load-balancing software.
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Table of contents
- CONTENTS
- PREFACE
- LIST OF TABLES
- LIST OF FIGURES
- CHAPTER 1 INTRODUCTION
- CHAPTER 2 SYNTHETIC WORKLOAD GENERATION
- CHAPTER 3 AUTOMATED LEARNING OF LOAD BALANCING STRATEGIES
- CHAPTER 4 CONCLUSIONS
- APPENDIX A A SURVEY OF STRATEGY LEARNING
- REFERENCES
- INDEX