Open AccessBook
Workload Modeling for Computer Systems Performance Evaluation
TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
The Analysis of Time Series: An Introduction
TL;DR: The analysis of time series: An Introduction, 4th edn. as discussed by the authors by C. Chatfield, C. Chapman and Hall, London, 1989. ISBN 0 412 31820 2.
Proceedings ArticleDOI
Large-scale cluster management at Google with Borg
TL;DR: A summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it are presented.
Proceedings ArticleDOI
Tailbench: a benchmark suite and evaluation methodology for latency-critical applications
Harshad Kasture,Daniel Sanchez +1 more
TL;DR: TailBench is presented, a benchmark suite and evaluation methodology that makes latency-critical workloads as easy to run and characterize as conventional, throughput-oriented ones, and a harness that implements a robust and statistically sound load-testing methodology.
Defining a session on Web search engines
TL;DR: In this article, the authors explore three alternative methods for detection of session boundaries and show that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions.
Journal ArticleDOI
Workload Characterization: A Survey Revisited
TL;DR: A comprehensive survey of the state of the art of workload characterization by addressing its exploitation in some popular application domains, focusing on conventional web workloads as well as on the workloads associated with online social networks, video services, mobile apps, and cloud computing infrastructures.
References
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Book ChapterDOI
Nonparametric Estimation from Incomplete Observations
Edward L. Kaplan,Paul Meier +1 more
TL;DR: In this article, the product-limit (PL) estimator was proposed to estimate the proportion of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t).
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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