Workload Modeling for Computer Systems Performance Evaluation

Dror G. Feitelson (Autor)

Buch | Hardcover

564 Seiten

2015
Cambridge University Press (Verlag)
9781107078239 (ISBN)

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This book closes the gap between experts and practitioners, emphasizing the intuition and reasoning behind definitions and derivations related to evaluations of computer systems performance. Readers will learn to analyze collected workload data, derive statistical models, including skewed marginal distributions and correlations, and consider generative models and feedback from systems.

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.

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.

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.

Zusatzinfo	18 Tables, unspecified; 90 Plates, color; 182 Line drawings, unspecified
Verlagsort	Cambridge
Sprache	englisch
Maße	182 x 260 mm
Gewicht	1140 g
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Informatik ► Weitere Themen ► Hardware
	Mathematik / Informatik ► Mathematik
ISBN-13	9781107078239 / 9781107078239
Zustand	Neuware