'Interarrival times' diaporamas de présentation

Chapter 20 Queuing Theory

Chapter 20 Queuing Theory. Description. Each of us has spent a great deal of time waiting in lines. In this chapter, we develop mathematical models for waiting lines, or queues. 8.1 Some Queuing Terminology.

1.5k views • 102 slides

Summarizing Measured Data

993 views • 99 slides

Inside a Router

Inside a Router. ECS 152A Acknowledgement: slides from S. Kalyanaraman & B.Sikdar. Overview. Network Layer Performance Modeling & Analysis Part I: Essentials of Probability Part II: Inside a Router Part III: Network Analysis.

807 views • 64 slides

Random-Number Generation

Random-Number Generation. Andy Wang CIS 5930-03 Computer Systems Performance Analysis. Generate Random Values. Two steps Random-number generation Get a sequence of random numbers distributed uniformly between 0 and 1 Random- variate generation

796 views • 43 slides

Detecting Conversing Groups of Chatters: A Model, Algorithm and Tests

382 views • 24 slides

Variance Reduction Techniques

Variance Reduction Techniques. Outline. Importance of Variance Reduction Types of Variance Reduction Techniques Common Random Numbers Example: Common Random Numbers Implementing Common Random Numbers in Arena Antithetic Variates Implementing Antithetic Variates in Arena Control Variates.

482 views • 21 slides

Queue theory

Queue theory. 1.1.????? ??? ?? ?? ????? ? ????? ? ??????? ????? ?? ( Queue ) ?? ?? ?????? ( Waiting Line ) ?? ??????? ? ?? ???? ????? ??? ????? ?? ?????? ???. ?? ????? ??? ???? ?? ?? ???? ?? ????:. 1.1 Examples. Example 1.1.1 Supermarket .

367 views • 21 slides

Outline

Outline. Chapter 3: Guided Tour Through Arena Chapter 4: Modeling Basic Operations and Inputs. Chapter 3 Guided Tour Through Arena. a drill press. Model 03-01. a drill press processing one type of product interarrival times ~ i.i.d. exp(5) service times ~ i.i.d. triangular (1,3,6)

222 views • 12 slides

Solutions Simulation 1

Solutions Simulation 1. 20.1-2) The weather can be considered a stochastic system, because it evolves in a probabilistic manner from one day to the next. Suppose for a certain location that this probabilistic evolution satisfies the following:

358 views • 8 slides

C12: The Poisson process

207 views • 7 slides