Sale!

Performance modeling and design of computer systems queueing theory in action 1st Edition by Harchol Balter ISBN 1107027500 9781107027503

Original price was: $50.00.Current price is: $35.00.

Performance modeling and design of computer systems queueing theory in action Harchol-Balter Digital Instant Download

Author(s): Harchol-Balter, Mor
ISBN(s): 9781107027503, 1107027500
Edition: Rep
File Details: PDF, 8.26 MB
Year: 2014
Language: english
SKU: EB-11844272 Category: Tags: ,

Performance modeling and design of computer systems queueing theory in action 1st Edition by Harchol Balter – Ebook PDF Instant Download/Delivery: 1107027500, 9781107027503
Full download Performance modeling and design of computer systems queueing theory in action 1st Edition after payment

Product details:

ISBN 10: 1107027500
ISBN 13: 9781107027503
Author: Harchol Balter

Tackling the questions that systems designers care about, this book brings queueing theory decisively back to computer science. The book is written with computer scientists and engineers in mind and is full of examples from computer systems, as well as manufacturing and operations research. Fun and readable, the book is highly approachable, even for undergraduates, while still being thoroughly rigorous and also covering a much wider span of topics than many queueing books. Readers benefit from a lively mix of motivation and intuition, with illustrations, examples and more than 300 exercises – all while acquiring the skills needed to model, analyze and design large-scale systems with good performance and low cost. The exercises are an important feature, teaching research-level counterintuitive lessons in the design of computer systems. The goal is to train readers not only to customize existing analyses but also to invent their own.

Performance modeling and design of computer systems queueing theory in action 1st Table of contents:

I. Introduction to Queueing

  1. Motivating Examples of the Power of Analytical Modeling
    1.1 What Is Queueing Theory?
    1.2 Examples of the Power of Queueing Theory

  2. Queueing Theory Terminology
    2.1 Where We Are Heading
    2.2 The Single-Server Network
    2.3 Classification of Queueing Networks
    2.4 Open Networks
    2.5 More Metrics: Throughput and Utilization
    2.6 Closed Networks
      2.6.1 Interactive (Terminal-Driven) Systems
      2.6.2 Batch Systems
      2.6.3 Throughput in a Closed System
    2.7 Differences between Closed and Open Networks
      2.7.1 A Question on Modeling
    2.8 Related Readings
    2.9 Exercises

II. Necessary Probability Background

  1. Probability Review
    3.1 Sample Space and Events
    3.2 Probability Defined on Events
    3.3 Conditional Probabilities on Events
    3.4 Independent Events and Conditionally Independent Events
    3.5 Law of Total Probability
    3.6 Bayes Law
    3.7 Discrete versus Continuous Random Variables
    3.8 Probabilities and Densities
      3.8.1 Discrete: Probability Mass Function
      3.8.2 Continuous: Probability Density Function
    3.9 Expectation and Variance
    3.10 Joint Probabilities and Independence
    3.11 Conditional Probabilities and Expectations
    3.12 Probabilities and Expectations via Conditioning
    3.13 Linearity of Expectation
    3.14 Normal Distribution
      3.14.1 Linear Transformation Property
      3.14.2 Central Limit Theorem
    3.15 Sum of a Random Number of Random Variables
    3.16 Exercises

  2. Generating Random Variables for Simulation
    4.1 Inverse-Transform Method
      4.1.1 The Continuous Case
      4.1.2 The Discrete Case
    4.2 Accept-Reject Method
      4.2.1 Discrete Case
      4.2.2 Continuous Case
      4.2.3 Some Harder Problems
    4.3 Readings
    4.4 Exercises

  3. Sample Paths, Convergence, and Averages
    5.1 Convergence
    5.2 Strong and Weak Laws of Large Numbers
    5.3 Time Average versus Ensemble Average
      5.3.1 Motivation
      5.3.2 Definition
      5.3.3 Interpretation
      5.3.4 Equivalence
      5.3.5 Simulation
      5.3.6 Average Time in System
    5.4 Related Readings
    5.5 Exercise

III. The Predictive Power of Simple Operational Laws

  1. Little’s Law and Other Operational Laws
    6.1 Little’s Law for Open Systems
    6.2 Intuitions
    6.3 Little’s Law for Closed Systems
    6.4 Proof of Little’s Law for Open Systems
      6.4.1 Statement via Time Averages
      6.4.2 Proof
      6.4.3 Corollaries
    6.5 Proof of Little’s Law for Closed Systems
      6.5.1 Statement via Time Averages
      6.5.2 Proof
    6.6 Generalized Little’s Law
    6.7 Examples Applying Little’s Law
    6.8 More Operational Laws: The Forced Flow Law
    6.9 Combining Operational Laws
    6.10 Device Demands
    6.11 Readings and Further Topics Related to Little’s Law
    6.12 Exercises

  2. Modification Analysis: “What-If” for Closed Systems
    7.1 Review
    7.2 Asymptotic Bounds for Closed Systems
    7.3 Modification Analysis for Closed Systems
    7.4 More Modification Analysis Examples
    7.5 Comparison of Closed and Open Networks
    7.6 Readings
    7.7 Exercises

IV. From Markov Chains to Simple Queues

  1. Discrete-Time Markov Chains
    8.1 Discrete-Time versus Continuous-Time Markov Chains
    8.2 Definition of a DTMC
    8.3 Examples of Finite-State DTMCs
      8.3.1 Repair Facility Problem
      8.3.2 Umbrella Problem
      8.3.3 Program Analysis Problem
    8.4 Powers of P: n-Step Transition Probabilities
    8.5 Stationary Equations
    8.6 The Stationary Distribution Equals the Limiting Distribution
    8.7 Examples of Solving Stationary Equations
      8.7.1 Repair Facility Problem with Cost
      8.7.2 Umbrella Problem
    8.8 Infinite-State DTMCs
    8.9 Infinite-State Stationarity Result
    8.10 Solving Stationary Equations in Infinite-State DTMCs
    8.11 Exercises

  2. Ergodicity Theory
    9.1 Ergodicity Questions
    9.2 Finite-State DTMCs
      9.2.1 Existence of the Limiting Distribution
      9.2.2 Mean Time between Visits to a State
      9.2.3 Time Averages
    9.3 Infinite-State Markov Chains
      9.3.1 Recurrent versus Transient
      9.3.2 Infinite Random Walk Example
      9.3.3 Positive Recurrent versus Null Recurrent
    9.4 Ergodic Theorem of Markov Chains
    9.5 Time Averages
    9.6 Limiting Probabilities Interpreted as Rates
    9.7 Time-Reversibility Theorem
    9.8 When Chains Are Periodic or Not Irreducible
      9.8.1 Periodic Chains
      9.8.2 Chains that Are Not Irreducible
    9.9 Conclusion
    9.10 Proof of Ergodic Theorem of Markov Chains
    9.11 Exercises

  3. Real-World Examples: Google, Aloha, and Harder Chains

  4. Exponential Distribution and the Poisson Process

  5. Transition to Continuous-Time Markov Chains

  6. M/M/1 and PASTA

People also search for Performance modeling and design of computer systems queueing theory in action 1st:

performance modeling and design of computer

analytical performance modeling and design of computer systems

15-857 analytical performance modeling and design of computer systems

performance modeling and design of computer systems pdf

performance modeling and design of computer systems solutions

Tags: Harchol Balter, design, computer systems