CS4 Modelling and Simulation
LN-9
9
9.1
Queueing Networks
Introduction
Mathematicians have studied queues for approximately 100 years, and one of their rst applications was to telephone exchanges (Erlangs loss formula ). However they gained popularity wit

CS4 Modelling and Simulation
LN-10
10
10.1
Solving Queueing Models
Introduction
In this note we look at the solution of systems of queues, starting with simple isolated queues. The benets of using predened, easily classied queues will become apparent: man

CS4 Modelling and Simulation
LN-11
11
Simulation ModelsIntroduction and Motivation
So far in the course the stochastic models which we have considered have been solved analytically. What this means is that by carrying out analysis of the system we have be

CS4 Modelling and Simulation
LN-12
12
SimJava
As discussed in the previous lecture note, process based simulation focuses primarily on entities within the system, rather than events. The model is developed as the series of interactions between these entit

CS4 Modelling and Simulation
LN-13
13
Random Variables and Simulation
In this lecture note we consider the relationship between random variables and simulation models. Random variables play two important roles in simulation models. We assume that within o

MODELLING WITH GENERALISED STOCHASTIC PETRI NETS
MODELLING WITH GENERALISED STOCHASTIC PETRI NETS
M. Ajmone Marsan
Politecnico di Torino
Gianfranco Balbo
Universit` di Torino a
Gianni Conte
Universit` di Parma a
Susanna Donatelli
Universit` di Torino a
Gi

Probability and Statistics with Reliability, Queuing and Computer Science Applications:
by K.S. Trivedi Publisher-John Wiley & Sons
second edition
Chapter 2:Discrete Random Variables Dept. of Electrical & Computer engineering Duke University Email: [email protected]

Probability and Statistics with Reliability, Queuing and Computer Science Applications
by K.S. Trivedi Publisher-John Wiley & Sons
Second edition
Chapter 7 : Discrete Time Markov Chains
Dept. of Electrical & Computer Engineering Duke University
Email: kst

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 1) :Continuous Time Markov Chains: Theory Dept. of Electrical & Computer engineering Duke Uni

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 2) :Continuous Time Markov Chain Availability Modeling Dept. of Electrical & Computer enginee

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 3) :Continuous Time Markov Chains Pure Performance Modeling Dept. of Electrical & Computer en

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 4) :Continuous Time Markov Chain Performability Modeling Dept. of Electrical & Computer engin

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 5) :Continuous Time Markov Chains Reliability Modeling Dept. of Electrical & Computer enginee

Probability and Statistics with Reliability, Queuing and Computer Science Applications
Second edition
by K.S. Trivedi Publisher-John Wiley & Sons
Chapter 8 (Part 6) :Continuous Time Markov Chains Solution Techniques Dept. of Electrical & Computer engineer

CS4 Modelling and Simulation
LN-8
8
Stochastic Process Algebra
In this lecture note we consider another class of performance modelling paradigms stochastic extensions of process algebras. Like queueing networks and stochastic Petri nets, and their variant

CS4 Modelling and Simulation
LN-7
7
Using a GSPN for Performance Evaluation
In this note we will consider two aspects of using a GSPN model of a system once it has been constructed: generating and solving a corresponding Markov process, and deriving perfo

CS4 Modelling and Simulation
LN-6
6
More about GSPN Models
In this note we will consider two simple systems modelled by GSPN and in the course of doing so examine more closely the dynamics of these models with respect to timed and immediate transitions. A

Statistics are like alienists cfw_ they will testify for either side.
Comparing Systems Using Sample Data
cfw_ Fiorello La Guardia
c 1994 Raj Jain
13.1
Old French word `essample' ) `sample' and `example' One example 6= theory One sample 6= De nite stateme

Instruction Slides for The Art of Computer Systems Performance Analysis
These slides are still in development. Some figures are missing from the slides. Not all parts or chapters are ready at this time. You can print two pages per sheet on most postscrip

CS4 Modelling and Simulation
LN-14
14
14.1
Model Validation and Verication
Introduction
Whatever modelling paradigm or solution technique is being used, the performance measures extracted from a model will only have some bearing on the real system represe

CS4 Modelling and Simulation
LN-15
15
15.1
The PC LAN as a SimJava Model
Introduction
In this note we consider again the PC LAN rst presented in lecture note 4 as a Markov process, and represented both as a GSPN and a PEPA model in subsequent lecture note

CS4 Modelling and Simulation
LN-16
16
Parameterisation and Workload Characterisation
So far in this course we have been concentrating on constructing a good representation of the system, appropriate for the investigation we wish to carry out. However it i

CS4 Modelling and Simulation
LN-17
17
17.1
Comparison of Techniques
Introduction
In this note we review the various approaches to representing a system which we have considered during the course, and try to identify their relative strengths and weaknesses

CS4 Modelling and Simulation
LN-1
1
1.1
Modelling and Simulation
Introduction
This course teaches various aspects of computer-aided modelling with an emphasis on the performance evaluation of computer systems and communication networks. The performance of