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Look Inside Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Name: Workload Modeling for Computer Systems Performance Evaluation
Price: 43.00 USD
Availability: LimitedAvailability
ISBN: 9781316236987

$43.00 ( ) USD

Author: Dror G. Feitelson, Hebrew University of Jerusalem

Date Published: February 2015
availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
format: Adobe eBook Reader
isbn: 9781316236987

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About the Authors

Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.
- Explains advanced statistical concepts like heavy tails and self similarity, emphasizing intuition and understanding
- Includes numerous illustrations created using real data sets from production computing systems
- Brings together material from diverse sources such as production computing systems, statistics, and computer science
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Product details
- Date Published: February 2015
- format: Adobe eBook Reader
- isbn: 9781316236987
- contains: 182 b/w illus. 90 colour illus. 18 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Introduction
2. Workload data
3. Statistical distributions
4. Fitting distributions to data
5. Heavy tails
6. Correlations in workloads
7. Self-similarity and long-range dependence
8. Hierarchical generative models
9. Case studies
10. Summary and outlook.
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Author
Dror G. Feitelson, Hebrew University of Jerusalem
Dror G. Feitelson is a Professor of Computer Science at the Hebrew University of Jerusalem. He is a founding co-organizer of a series of international workshops on job-scheduling strategies for parallel processing and of the ACM Experimental Computer Science Workshop. He maintains the Parallel Workloads Archive, a widely used community resource with logs of activity on parallel supercomputers.