Workload Modeling for Computer Systems Performance Evaluation
Published on 27. February 2015
978-1-316-23698-7 (ISBN)
Description
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
18 Tables, unspecified; 90 Plates, color; 182 Line drawings, unspecified
File size
18,47 MB
ISBN-13
978-1-316-23698-7 (9781316236987)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Dror G. Feitelson
Workload Modeling for Computer Systems Performance Evaluation
Book
03/2015
Cambridge University Press
€53.22
Article exhausted; check different version
Person
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.
Content
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.
