Our Questions and Answers

Published on June 2016 | Categories: Documents | Downloads: 47 | Comments: 0 | Views: 283
of 35
Download PDF   Embed   Report

Comments

Content


NAME: ADEFEMI FOLAMOLUWA 
MATRIC NO: 090805002 
ANALYSIS OF SIMULATION OUTPUT DATA 
1. Explain Simulation and the need for it? 
Simulation can be defined as the designing of a proposed or existing system, executing 
this model on a computer and analyzing the output gotten from the execution of the 
model. 
Most times, simulation is carried out because the physical system does not exist, cost of 
building an actual system is high or because measuring an actual system is time 
consuming. In all, simulations of systems, proposed or existing is to analyze and predict 
to a great extent, the functionality of the system. 
 
2. Can classical statistical methods be used to analyze simulation output data? Give 
reasons to back up your answer 
Classical statistical methods cannot be used to analyze simulation output data. This is 
because simulation output almost never produce 
•  raw independent (data from simulation runs are most times correlated),  
•  identically distributed  
•  normal data.  
Classical statistical techniques based on independent, identically distributed techniques 
are therefore not applied for correct system inferences. 
 
3. State and explain the types of simulations with respect to output analysis 
Finite‐Horizon Simulations: 
The termination of a finite‐horizon simulation takes place at a specific time or is caused 
by the occurrence of a specific event.  
 
Steady‐state simulations: 
In this type of simulation, long‐term behaviors of systems are analyzed. A performance 
measure is therefore called a steady‐state parameter if it is a characteristic of the 
equilibrium distribution of an output stochastic process. 
 
 
4. Give 2 examples each of the above listed kind of simulations 
Finite‐Horizon Simulations: 
•  Mass transit system between during rush hour. 
•  Production system until a set of machines breaks down. 
•  Start‐up phase of any system  
 
Steady‐state simulations: 
•  Continuously operating communication system where the objective is the 
computation of the mean delay of a packet in the long run. 
•  Distribution system over a long period of time. 
 
5. Why is the analysis of Simulation output data necessary after all system simulations? 
Simulation detects design errors of systems already built or intended to be built before 
the system is released to the users. Therefore, for correct analysis of the system, output 
of the system simulation has to be correctly analyzed. If the output is analyzed wrong, 
the system will not behave as expected and can invalidate all results. 
 
Adibiologun funke oluwaseun 
090805005 
Csc524 
Question 
 
(1) What does queue mean 
(2) Difference between a queuing network and a network of queue 
(3) List and describe the three types of queuing network 
(4) Explain the term birth‐death process 
(5) List the two earliest queuing model of computer systems 
 
Answer 
(1) A queue occurs when a potential customers arrives at a system that offers certain service 
that the customers wish to use. In computer systems, many jobs share the same resources 
such as CPUs, disks, and other devices. Since generally only one job can use the resource at 
any time, all other jobs wanting to use the system wait in queues. 
(2) A queuing network is a model in which jobs departing from one queue arrive at another 
queue or possibly the same queue while network of queue is a collection of service centers, 
which represent system resources, and customers, which represent users or transactions.  It 
is a network consisting of interconnected queues. 
(3)  Open  
Closed  
Mixed 
An open queuing network is the one that has external arrivals and departures. i.e. it receive 
customers from an external source and send them to an external destination. The job enters 
the system as “IN” and exits as “OUT”. The number of jobs in the system varies with time.  
 
A closed queuing network is the one that has no external arrivals and departures. It has 
constant numbers of  customers (finite population). They have a fixed population that  
moves between the queues but never leaves the queue. The jobs in the queue keep  
circulating from one queue to the next. The jobs exiting the system immediately reenter the 
system. The flow of jobs in the Out‐to‐In link defines the throughput of the closed system. 
 
Mixed queuing network are networks  that are open  for some workloads and closed for 
other. i.e it is open for some classes and closed for  others. 
(4) It is a process that is used to model a system in which jobs arrive one at a time. The state of 
a system can be represented by number of jobs n in the system. Arrival of a new job 
changes to n+1. This is called a Birth. Similarly the departure of jobs changes the system 
state to n‐1. This is called a Death. Therefore the number of jobs in such system can be 
modeled as a birth‐death process  
(5) Machine repairman model 
Central server model 
 
 
 
 
 
 
 
 
 
AMUDA, Tosin Joseph
090805009
CSC 524
Question 1: List the three-step approached for ode! "a!idation as proposed #$ %a$!or and &in'er(
Answer
%a$!or and &in'er for)!ated a three-step approach to ode! "a!idation that has #een *ide!$ fo!!o*ed(
Step +, -)i!d a ode! that has hi'h face "a!idit$,
Step 2, .a!idate ode! ass)ptions,
Step /, Copare the ode! inp)t-o)tp)t transforations to correspondin' inp)t-o)tp)t transforations
for the rea! s$ste,
Question 2: 0hen is a ode! said to ha"e hi'h face "a!idit$1
Answer
A ode! that has high face validity is a ode! that appears to #e a reasona#!e iitation of a rea!-*or!d
s$ste to peop!e *ho are 2no*!ed'ea#!e of the rea! *or!d s$ste,

&ace "a!idit$ is tested #$ ha"in'
)sers and peop!e 2no*!ed'ea#!e *ith the s$ste e3aine ode! o)tp)t for reasona#!eness and in the
process identif$ deficiencies, An added ad"anta'e of ha"in' the )sers in"o!"ed in "a!idation is that the
ode!4s credi#i!it$ to the )sers and the )ser4s confidence in the ode! increases,
A ode! *ith hi'h sensiti"it$ to ode! inp)ts can a!so #e said to ha"e a hi'h face "a!idit$, &or
e3ap!e, if a si)!ation of a fast food resta)rant dri"e thro)'h *as r)n t*ice *ith c)stoer arri"a!
rates of 20 per ho)r and 40 per ho)r then ode! o)tp)ts s)ch as a"era'e *ait tie or a3i) n)#er
of c)stoers *aitin' *o)!d #e e3pected to increase *ith the arri"a! rate
Question 3: 53p!ain the ters .a!idation and .erification
Answer
.erification is deterinin' that a si)!ation cop)ter pro'ra perfors as intended, i,e,, de#)''in'
the cop)ter pro'ra, 6t deterines if *e ha"e a #)i!t a si)!ation ode! correct!$,
.a!idation is concerned *ith deterinin' *hether the concept)a! si)!ation ode! 7as opposed to the
cop)ter pro'ra8 is an acc)rate representation of the s$ste )nder st)d$, 6t deterines if *e ha"e
#)i!t a correct ode!,
Question 4: 0hat do $o) )nderstand #$ str)ct)ra! ass)ption and *h$ sho)!d iit #e "a!idated1
Answer
Structural Assumptions
Ass)ptions ade a#o)t ho* the s$ste operates and ho* it is ph$sica!!$ arran'ed are str)ct)ra!
ass)ptions, &or e3ap!e, the str)ct)re of a 9)e)in' s$ste( *hether it is a first coe first ser"e 9)e)e
or a priorit$ 9)e)e,
Man$ str)ct)ra! pro#!es in the ode! coe fro poor or incorrect ass)ptions, To a"oid str)ct)ra!
pro#!es in a ode!, the str)ct)ra! ass)ptions )st #e "a!idated, 6f possi#!e the *or2in's of the
act)a! s$ste sho)!d #e c!ose!$ o#ser"ed to )nderstand ho* it operates, The s$stes str)ct)re and
operation sho)!d a!so #e "erified *ith )sers of the act)a! s$ste,
Question 5: Descri#e the difference #et*een ode! "erification and "a!idation,
Answer
.erification : did $o) #)i!d it ri'ht 7to the specification8, "a!idation : did $o) #)i!d the ri'ht thin' 7to
re9)ireents8,
.erification is done on the cop)teri;ed ode! *hi!e "a!idation is done on the concept)a! ode!,
.erification in"o!"es )sin' soft*are en'ineerin' 9)a!it$ ass)rance techni9)es *hi!e "a!idation a2es
)se of o#<ecti"es statistica! techni9)es,
Questions and Answer
1. Briefly explain the six characteristics of a single server system provided by Kendall
Answer: A/S/c/m/N/SD


i. Arrival Pattern of Customers: Before customers can be processed or subjected to
waiting, they must first enter the system. They can arrive smoothly or in an
unpredictable fashion. They can arrive one at a time or in clumps. A special
arrival process, which is highly useful for modeling purposes, is the Markov
arrival process.

ii. Service Time Distribution: The service time is the time which a server spends
satisfying a customer. If the average duration of a service interaction between a
server and a customer is 1/µ then µ is the service rate.

iii. Server: In a single server queue, the service facility can only serve one customer
at a time, waiting customers will stay in the buffer until chosen for service; how
the next customer is chosen will depend on the service discipline.

iv. Buffer Capacity: Customers who cannot receive service immediately due to
unavailability of the server must wait in the buffer. This leads to buffer being
filled up if the buffer has a finite capacity. In some systems the buffer capacity is
so large as to never affect the behavior of the customers; in this case the buffer
capacity is assumed to be infinite.

v. Service Discipline: When more than one customer is waiting for service there has
to be a rule for selecting which of the waiting customers will be the next one to
gain access to a server. The commonly used service disciplines are:
 FCFS first-come-first-serve (or FIFO first-in-first-out).
 LCFS last-come-_first-serve (or LIFO last-in-first-out).
 RSS random-selection-for-service.
 PRI priority. The assignment of different priorities to elements of a population is
one way in which classes are formed.
vi. Population: The characteristic of the population which we are interested in is
usually the size. Clearly, if the size of the population is fixed, at some value N, no
more than N customers will ever be requiring service at any time.

2. What is Throughput of a single server queue
Answer: Throughput of a single server queue is the average number of jobs that depart from
the queue per unit time (after they have been serviced).

3. What is Traffic Intensity?
Answer: The two most important features of a single server queue are the arrival rate of
customers ᴧ, and the service rate of the server(s), µ. These are combined into a single
parameter which characterizes a single or multiple server system, the traffic intensity.
Traffic intensity, ƿ =




4. State Little’s law
Answer: Little’s law states that under steady state conditions, the average number of items in
a queuing system equals the average rate at which items arrive multiplied by the time that an
item spends in the system. Letting
L =average number of items in the queuing system
W= average waiting time in the system for an item, and
ᴧ = average number of items arriving per unit time, the law is
L = ᴧW
5. Describe the two types of queuing networks.
Answer: Open and closed queuing networks.
In an Open queuing model, jobs enter the network at random from outside at a fixed rate,
receive service at one or more nodes, and eventually leave the network. Thus, the total
external arrival rate or throughput is an independent variable and the number of jobs in
the system is a dependent variable. The total number of jobs in the system varies with
time while,
In a closed queuing model, there is a fixed population of jobs in the network. The number
of jobs in the system is an independent variable and the throughput is a dependent
variable.






DURU DUMEBI JULIAN 
090805021 
 
Network Modelling questions 
 
1. Why is network modelling important? 
 
Answer 
‐ Helps in gaining knowledge on the level of performance of a system 
‐ Prevents unnecessary overloading of node points 
‐ Helps network administrators in maintaining Computer networks 
 
2. Discuss the factors affecting network communication. 
 
Answer 
They are grouped in two: 
i. Geometric conditions 
‐ Distance of network nodes 
‐ Materials of the transfer medium 
‐ Number of users 
 
3. State the characteristics of computer networks required for faultless data transfer 
 
Answer 
‐ Transmission times of packets with the same longitude may be different. 
‐ Data transfer sections running in parallel with each other do not affect each other directly, 
but in the nodes, for example the appearance of multiplied packets makes disturbing effects. 
‐ Two‐way traffic does not exist. 
‐ The intensity of inner communication changes in time between the nodes directly 
connected to each other.  
‐ To control affecting message transmission, an inner communicational system works 
between the nodes connected to each other. For example, the receiver can receive a 
message vainly if the transmitter does not have a message to be sent on. 
 
4. Why is Queuing modelling important to network modelling 
 
Answer 
Queuing modelling is important to network modelling as it gives an indication on the rate of 
arrival of data to a node, the rate at which data is being processed and the probability of 
congestion. 
5. Discuss the parameters to consider in queuing analysis. 
 
Answer 
‐ Population size 
‐ Number of servers 
‐ System capacity 
‐ Arrival process 
‐ Service time distribution 
‐ Service discipline 
 
5 questions for 524
1. What is simulation? Why do we analyze simulation output data?
2. What is the problem of simulation and how can it be avoided when analyzing?
3. Briefly discuss the types of simulation output data analysis
4. What is the initialization bias problem? In what type of analysis does it present itself and why?
5. Briefly discuss the ways of getting rid of initialization bias.

Answers
1. It is an imitation (logical or physical) of the operation of real world problems. It consists of several steps: data
collection, coding and verification, model validation, experimental design, output data analysis and implementa
tion.
b. We analyze simulation output data to approximate/estimate system parametres so that we can identify parame
tre values that optimize some system performance measures

2. The problem of simulation is that they almost never produce raw output that is independent and identicall dist
ributed normal data e.g for independent, during a simulation, all customers will probably wait the same amount
of time on a queue.
b. For finite state analysis, it can be avoided by running independent replications of the output data to avoid the
variance being biased. Therefore we apply classical statistics to the replications and not the observations. For ste
ady state, we can avoid it in the batch method by making the m observation in each batch large enough.

3. a. Finite state simulation: These are for systems which never reach a steady state and terminate after a finite p
eriod of time interval. We estimate prarameters here based on the specifix initial and stopping conditions.. When
analyzing the data, we run the simulation n times, each time using a different random number stream to ensure i
ndependent trials. Then we apply classic statistics to estimate the mean and variance based on the replucated dat
a (from the n runs). We approximate the performance of these systems using the performance measures from the
different independent runs.
b. Steady state simulation: This is more complex than the finite state simulation. We estimate the operation in th
e long run. This does not depend on initial conditions so we must ensure that the simulation is run long enough s
o that the effecta of the initial conditions are gone. We will analyse using a method known as batch means. Here
, we divide one long simulation into a number of contiguous batches i.e divide batch n into a number of contigu
ous batches each with m observations. We estimate the mean and variance based on these batches. If the m obse
rvations are long enough, the data is independent and identically distributed normal data.

4. One must provide initial values for the simulation variables before running a simulation. The values are chose
n randomly since the experimenter may not know what values are appropriate. This can have a significant but un
recognized impact on the output of the simulation.
b. It presents itself particularly in the steady state out analysis because it can lead to point estimators having mea
n squared error.

5. a. We can truncate thw output by allowing the simulation to warm up before retaining data for analysis. It is p
robably the most popular and major simulation languages come with built in truncation functions. If the output i
s truncated too early, bias will still exist, if teuncated too late, then good observations might be wasted. The expe
rimenter can therefore average observations and choose a truncation point based on the average.
b. We can make a very long run to overwhelm the effects of initialization bias. This is simple to carry out and m
ay yield point estimators with less mean squared errors. However, this method can be wasteful with observation
s.
NAME: OPEOLUWA J OSEPH OLUWATOBI
DEPARTMENT: COMPUTER SCIENCES
MATRIC NO: 090805048
LEVEL: 500

QUEUEING NETWORKS
QUESTIONS
1. What is Queueing Network?
2. What are the types of Queueing Networks?
3. Explain three criteria satisfied by product form networks?
4. What do you understand by Routine Homogeneity?
5. What are the two queueing models of a computer system?

ANSWERS
1. What is Queueing Network
This is a network consisting of several interconnected queues. It is also a model in which jobs
departing from one queue arrive at another queue or possibly the same queue.

2. What are the types of Queueing Networks?
Open Queueing Networks
Closed Queueing Networks
Mixed Queueing Networks
OPEN QUEUEING NETWORK
This is a type of network that has external arrivals and departures. The jobs enter the system
at “In” and exit at “Out.” The number of jobs in the system varies with time. In analyzing an
open system, we assume that the throughput is known (to be equal to the arrival rate), and the
goal is to characterize the distribution of number of jobs in the system.
CLOSED QUEUEING NETWORK
This is a network that has no external arrivals or departures. The jobs in the system keep
circulating from one queue to the next. The total number of jobs in the system is constant. It
can also be view a closed system as a system where the Out is connected back to the In. The
jobs exiting the system immediately reenter the system.
MIXED QUEUEING NETWORK
This is a network that is open for some workloads and closed for others. In a mixed queueing
network, all jobs of a single class have the same service demands and transition probabilities.
Within each class, the jobs are indistinguishable.

3. Explain three criteria satisfied by product form networks?

Service discipline
J ob classes
Service Time Distribution

a. Service discipline
Service Disciplines: All service centers have one of the following four types of service
disciplines: First Come, First Served (FCFS), Processor Sharing (PS), Infinite Servers
(ISs or delay centers), and Last Come, First Served Preemptive Resume (LCFS-PR).


b. . J ob Classes:
The jobs belong to a single class while awaiting or receiving service at a service center
but may change classes and service centers according to fixed probabilities at the
completion of a service request.

c. Service Time Distributions:
At FCFS service centers, the service time distributions must be identical and exponential
for all classes of jobs. At other service centers, where the service times should have
probability distributions with rational Laplace transforms, different classes of jobs may
have different distributions.

4. What do you understand by Routine Homogeneity?
The routing homogeneity condition implies that the probability of a job going from one device to
another device does not depend upon the number of jobs at various devices.

5. What are the two queueing models of a computer system?
Machine repairman model
central server model
MACHINE REPAIRMAN MODEL
The machine repairman model, as the name implies, was originally developed for modeling
machine repair shops. It has a number of working machines and a repair facility with one or
more servers (repairmen). Whenever a machine breaks down, it is put in the queue for repair and
serviced as soon as a repairman is available.
CENTRAL REPAIRMAN MODEL
This is a model that was introduced by Buzen (1973). The CPU is the central server that
schedules visits to other devices. After service at the I/O devices the jobs return to the CPU.

OSINUGA OLUWASEUN DANIEL
090805049
CSC 524 QUESTIONS AND ANSWERS
1. Give 3 advantages and 3 practical applications of simulation

ADVANTAGES
A. Simulation helps us to study new designs without interrupting real system.
B. Simulation helps us to study new designs without needing extra resources
C. Simulation is less dangerous / expensive / intrusive.
D. Simulation helps us to improve the understanding of the system.
E. Simulation helps us to manipulate time.

PRACTICAL APPLICATIONS
1. Analysis of air pollutant dispersion using atmospheric dispersion modeling
2. Design of complex systems such as aircraft and also logistics systems.
3. Design of noise barriers to effect roadway noise mitigation
4. Flight simulators to train pilots
5. Weather forecasting
6. Forecasting of prices on financial markets (for example Adaptive Modeler)
7. Behavior of structures (such as buildings and industrial parts) under stress
and other conditions
8. Design of industrial processes, such as chemical processing plants
9. Strategic management and organizational studies
10. Reservoir simulation for the petroleum engineering to model the
subsurface reservoir
11. Process engineering simulation tools.
12. Robot simulators for the design of robots and robot control algorithms
13. Urban simulation models that simulate dynamic patterns of urban
development and responses to urban land use and transportation policies.
See a more detailed article on Urban Environment Simulation.
14. Modeling car crashes to test safety mechanisms in new vehicle models.
15. Crop-soil systems in agriculture, via dedicated software frameworks (e.g.
BioMA, OMS3, APSIM)


2. Differentiate between a deterministic and stochastic Model
A deterministic Model is a model which, given a particular input, will always
produce the same output, with the underlying machine always passing through
the same sequence of states.

Stochastic Model is a model in which ranges of values for each variable (in the
form of probability distribution) are used.

3. What are the steps to simulation study


4. Differentiate between Verification and Validation
 Verification is the process of determining that a model implementation
accurately represents the developer’s conceptual description of the model and
the solution to the model.
 Validation is the process of determining the degree to which a model is an
accurate representation of the real world from the perspective of the intended
uses of the model.

5. What is discrete event simulation
Discrete event simulation (DES) is the process of modelling the behavior of a
complex system (mathematically or logically) as an ordered sequence of well-
defined events. In this context, an event comprises a specific change in the
system's state at a specific point in time.

NAME: OYEWOLE, Mopelola O.
MATRIC NO.: 090805054
DEPARTMENT: COMPUTER SCIENCES
COURSE: CSC 524
ASSIGNMENT: QUESTIONS ON
PERFORMANCE MODELING
LECTURER: ADEWOLE, A. P. (DR.)
Questions
1a. What is performance modeling?
Answer
Performance modeling is a structured and repeatable approach to modeling the performance
of your software. It begins during the early stages of an application design and continues
throughout the application life cycle.
b. What is the goal of performance modeling?
Answer
The goal of performance modeling is to gain understanding of a computer system's
performance on various applications, by means of measurement and analysis, and then to
wrap up these characteristics in a compact formula.
2a. What are the benefits of performance modeling?
Answer
The benefits of performance modeling are:
 Performance becomes a feature of our development process and not an afterthought.
 Modeling helps answer the question "Will our design support our performance
objectives?" We can evaluate our tradeoffs earlier in the life cycle before we actually
build and analyze models.
 We know explicitly what design decisions are influenced by performance and the
constraints performance puts on future design decisions. If these decisions are not
captured, it can lead to maintenance efforts that work against our original goals.
 Surprises are avoided in terms of performance when our application is released into
production.
 We end up with a document of itemized scenarios that help us to quickly see what is
important. That translates to where to instrument, what to test for, and how to know
whether we are trending toward or away from the performance goals throughout our
application life cycle.
b. Mention at least 5 things that performance modeling reveals about an application.
Answer
Performance modeling reveals the following about an application:
 The relevant code paths and how they affect performance.
 Where the use of resources or computations affect performance.
 The most frequently executed code paths. This helps us identify where to spend time
tuning.
 The key steps that access resources and lead to contention.
 Where our code is in relation to resources (local, remote).
 The tradeoffs we have made for performance.
 The components that have relationships to other components or resources.
 Where our synchronous and asynchronous calls are.
 What our I/O-bound work and CPU-bound work are.
3a. What best practices should be considered when creating performance models?
Answer
We should consider the following best practices when creating performance models:
 Determine response time and resource utilization budgets for our design.
 Identify our target deployment environment.
 Do not replace scenario-based load testing with performance modeling, for the
following reasons:
o Performance modeling suggests which areas should be worked on but cannot
predict the improvement caused by a change.
o Performance modeling informs the scenario-based load testing by providing
goals and useful measurements.
o Modeled performance may ignore many scenario-based load conditions that
can have an enormous impact on overall performance.
b. What is the information in the performance model?
Answer
The information in the performance model is divided into different areas/categories. They
are:
 Application Description: The design of the application in terms of its layers and its
target infrastructure.
 Scenarios: Critical and significant use cases, sequence diagrams, and user stories
relevant to performance.
 Performance Objectives: Response time, throughput, resource utilization.
 Budgets: Constraints we set on the execution of use cases, such as maximum
execution time and resource utilization levels, including CPU, memory, disk I/O, and
network I/O.
 Measurements: Actual performance metrics from running tests, in terms of resource
costs and performance issues.
 Workload Goals: Goals for the number of users, concurrent users, data volumes, and
information about the desired use of the application.
 Baseline Hardware: Description of the hardware on which tests will be run in terms
of network topology, bandwidth, CPU, memory, disk, and so on.
Other information that might be needed are:
 Quality-of-Service (QoS) Requirements: QoS requirements, such as security,
maintainability, and interoperability, may impact our performance. We should have
an agreement across software and infrastructure teams about QoS restrictions and
requirements.
 Workload Requirements: Total number of users, concurrent users, data volumes,
and information about the expected use of the application.
4a. What are the inputs required for the performance modeling process?
Answer
The inputs required for the performance modeling process include initial (maybe even
tentative) information about the following:
 Application design and target infrastructure and any constraints imposed by the
infrastructure.
 Scenarios and design documentation about critical and significant use cases.
 QoS requirements and infrastructure constraints, including service level agreements
(SLAs).
 Workload requirements derived from marketing data on prospective customers.
b. What are the outputs from performance modeling?
Answer
The output from performance modeling is the following:
 A performance model document.
 Test cases with goals.
Performance Model Document
The performance model document may contain the following:
 Performance objectives.
 Budgets.
 Workloads.
 Itemized scenarios with goals.
 Test cases with goals.
An itemized scenario is a scenario that we have broken down into processing steps. For
example, an order scenario might include authentication, order input validation, business
rules validation, and orders being committed to the database. The itemized scenarios include
assigned budgets and performance objectives for each step in the scenario.
Test Cases with Goals
We use test cases to generate performance metrics. They help to validate our application
against performance objectives. Test cases help us to determine whether we are trending
toward or away from your performance objectives.
5. The performance modeling process is in how many steps? List and explain briefly.
Answer
The performance modeling process is in eight (8) steps. They are:
1. Identify Key Scenarios
Identify scenarios where performance is important and scenarios that pose the most risk to the
performance objectives.
2. Identify Workload
Identify how many users and concurrent users the system needs to support.
3. Identify Performance Objectives
Define performance objectives for each of the key scenarios. Performance objectives reflect
business requirements.
4. Identify Budget
Identify the budget or constraints. This includes the maximum execution time in which an
operation must be completed and resource utilization constraints, such as CPU, memory, disk
I/O, and network I/O.
5. Identify Processing Steps
Break down the key scenarios into component processing steps.
6. Allocate Budget
Spread the budget (determined in Step 4) across the processing steps (determined in Step 5)
to meet the performance objectives (defined in Step 3).
7. Evaluate
Evaluate the design against objectives and budget. There may be the need to modify the
design or spread the response time and resource utilization budget differently to meet the
performance objectives.
8. Validate
Validate the model and estimates. This is an ongoing activity and includes prototyping,
assessing, and measuring.
ADEYEMI MONSURAT ADEOLA 100805008
Questions on Analysis of Single Server Queue and Queue Networks.
1. What is a Single Server Queue?
2. What is a Queue Network?
3. Differentiate between Open, Closed and Mixed Queueing Networks?
4. Describe the Birth-Death Process?
5. Why do we analyse single server queues?

Solutions
1. The simplest queuing model is one that has only one queue. Such a model can be used to
analyse individual resources in computer systems. The central element of the system is a
server, which provides some service to items.

2. A queueing network describes the system as a set of interacting resources.
Queueing networks can also be defined as a model in which jobs departing from one queue
arrive at another queue (or possibly the same queue).

3. In an Open queueing Network, it has both external arrivals and departures where jobs enter
the system at “In” and exit at “Out”. In a Closed queueing Network, It has no external
arrivals or departures but the jobs in the system keep circulating from one queue to the
next. The total number of jobs in the system is constant. In a Mixed queueing Network,
they are open for some workloads and closed for others. The system is closed for
interactive jobs and is open for batch jobs.

4. A birth-death process is useful in modelling systems in which jobs arrive one at a time (and
not as a batch). The state of such a system can be represented by the number of jobs n in
the system, Arrival of a new job changes the state to n + 1. This is called a birth. Similarly,
the departure of a job changes the system state to n – 1. This is called a death. The number
of jobs in such a system can therefore be modelled as a birth-death process.


5. We analyse a Single server queue to determine the item population, queue size and
dispatching discipline of the queue.

Name: Fapohunda Oluwaseun
Matric Number: 100805032
Course : CSC 524


Questions on Validation and Verification of Simulation Models

Questions
1. Define System Validation and Verification

Validation: determines that the theories and assumptions underlying the conceptual
model are correct, it asks the question: are we building the right model?

Verification: ensures that the computer programming and implementation of the
conceptual models are correct, verification is concerned with ensuring that are there
are no errors in simulation. Verification asks the question: are we building the model
right?

2. Outline the approaches for Validation of models (as proposed by Naylor and Finger)
(i) Develop a model with High Face Validity.
(ii) Test the assumptions of the model.
(iii) Determine how representative the simulation output data are.

3. What is Face Validation
Face Validation is a process where experts of the problem entity domain check the
conceptual model to see if it is correct and reasonable for its purpose.

4. In Verification of Simulation models, Simulation languages are preferred to regular high level
programming languages (e.g Java) for model implementation, Why?
The use of Simulation language will result in easier implementation, reduce
programming time and fewer errors are encountered.
5. What is the expected outcome of the Validation and Verification process?
The expected outcome of the model validation and verification process is the
quantified level of agreement between experimental data and model prediction, as
the predictive accuracy of the model.




Define Model Validation
Outline the steps taken to perform validation
Questions on modeling of computer system networks 
1)  Describe the temporal behavior of any system 
Answer‐ The temporal behavior of a system can be gotten by evaluating the time needed by an entity to 
cross the system. This time has two main components: the strict time needed for its execution in the 
different hardware components and the time spent waiting either to use some resource because it is 
used by another entity or the arrival of some other entities to some synchronization points. 
2) Modeling techniques uses mathematical methods to tackle sources of delay in a system. Briefly list 
these methods 
Answer‐ Queuing Networks, Petri nets and Process Algebras 
3a) What is a Queuing Network?  
Answer‐ A queuing network can be described as a model in which jobs depart from one queue and 
arrive at another queue or at the same queue. This is represented by connecting the output of one 
queue to the input of another queue 
3b) List and explain the types of Queuing Networks 
Answer‐ There are two types of queuing networks.  
Open Queuing Networks & Closed Queuing Networks 
Open Queuing networks ‐ An open queuing network is one in which jobs enter the system at a particular 
point and exit the system at a different point i.e. it has external arrivals and departures. The source has 
an indefinite number of jobs. The particular number of jobs in the system is not constant. It varies with 
respect to time. 
Closed Queuing Networks ‐ A closed queuing network is one in which a fixed number of jobs circulate 
within the nodes of the network (from one queue to the other). No new job enters or leaves the 
network system i.e. it has no external arrivals or departures.  The number of jobs in the system is 
constant. 
4) What are Petri nets? 
Answer ‐ Petri nets are a formalism for the description of the concurrency and synchronization inherent 
in computer (and other interacting) systems. Petri nets are directed graphs with two types of nodes:  
places (circles), transitions (bars) and unidirectional arcs (arrows) between them. Tokens move between 
places according to the firing rules imposed by the transitions. A transition can fire when each of the 
places connected to it has at least one token. When it fires, the transition removes tokens from each of 
these places and deposits tokens in each of the places it is connected to. 
5) What are process algebras and give examples? 
Answer ‐ Process algebras are abstract languages which have been introduced for the specification and 
understanding of complex systems with concurrent phenomena. These mathematical theories provide 
apparatus for reasoning about the structure and behavior of the model, as qualitative system 
properties. Examples include the Calculus of Communicating Systems (CCS), Communicating Sequential 
Processes (CSP) and the Algebra of Communicating Processes (ACP) 
Kesa Oluwafunmilola 
Questions 
1. What are the parameters that affect network performance? 
Throughput, latency, bit error rate and jitter 
2. Briefly explain three kinds of models with practical examples. 
a. Physical model: 
A physical model is one which is usually a physical replica, often on a reduced scale, 
of the system it represents. Examples include a model of an airplane (scaled down), 
a model of the atom (scaled up). 
 
b. Simulation model: 
Simulation model mimics the real behaviour of the object or a system. It involves 
designing a model of an actual or theoretical physical system, executing the model 
on a computer, and analysing the execution results. Example is a war game. 
 
c. Analytical model: 
Analytical model does not represent operations that mimics the behaviour of the 
object, rather mathematical operations are used to capture the relationships. For 
example, bank customer’s waiting time analysis 
 
3. Mention three mathematical techniques used in performance modelling of computer 
networks. 
a. Queuing Networks 
b. Petri Nets 
c. Process algebras 
 
4. What is Markov chain and what is it used for?  
Markov chain refers to the sequence of random variables such a process moves through, 
with the Markov property defining serial dependence only between adjacent periods.  
Markov chain is used for describing systems that follow a chain of linked events, where what 
happens next depends only on the current state of the system. 
 
5. Mention three solving techniques of performance metrics for Computer networks. 
a. Markov chains 
b. Simulation 
c. Analytical methods 
 
ANALYSIS OF SIMULATION OUTPUT
(MATTHEW OMOLABAKE)

1. Give a brief description of the two types of simulation w.r.t output analysis
b. What is the objective of analysis of the two types of simulation?
a. There are two types of simulations with respect to output analysis: terminating and
non-terminating (steady state). The type of analysis depends on the goal of the study.
Terminating simulation is one where there is a specific starting and stopping condition
that is part of the model e.g. a bank with an opening time of 8.am and closing time of
5pm. The objective of analysis of terminating simulations is to obtain a point estimate
(sample mean) and confidence interval for some parameter (average time in system for n
customers, machine utilization, work-in-progress etc). Confidence interval for
terminating simulations usually uses independent replications.

While a steady state simulation is one where there is no specific starting and ending
conditions e.g. an emergency room. Here we are interested in the steady state behavior of
the system. The objective here is to estimate the steady state mean.


b. Because simulation output are independent and identically distributed normal data ,the
purpose of the analysis is to give methods to perform statistical analysis of output by
 Estimating the standard error or confidence interval
 Figure out the number of observations required to achieve desired error.



2. What are the problems that may arise when analyzing a non-terminating simulation?
 How should the simulation be started (initialization at time zero)
Before a simulation can be run, one must provide initial values for all of the simulation’s
state variables. The choice of initial conditions can have a significant but unrecognized
impact on the simulation run’s outcome.

 How long must it run before data representative of steady state can be collected
The basic question here is should you do many short runs or one long run?


3. Give two methods each used to detect and deal with initialization bias
Detecting:
a. Detecting the bias visually by scanning a realization of the simulated process.
b. Conduct statistical test for initialization bias
Dealing:
a. Truncate the output by allowing the simulation to “warm up” before data are
retained for analysis. Experimenter hopes that the remaining data are
representative of the steady state system.
b. Make a long run to overwhelm the effects of initialization bias.

4. Give a brief description of analysis of non-terminating simulation output using many
short runs and one long run
 Many short runs
The analysis is exactly the same as for terminating systems. The (1-a) %
confidence interval is computed before. The problem here is that because of initial
bias, the sample mean may no longer be an unbiased estimator for the steady state
mean.
 One long run
Make just one long replication so that the initial bias is only introduced once. This
way, you will not be “throwing out” a lot of data. The problem here is how you
estimate the variance because there is only one run.


5. State the merits and demerits of using many short runs and one long run respectively.
 Many short runs
Advantages
1. Simple analysis, similar to the analysis of terminating systems
2. The data from different replications are independent and identically
distributed
Disadvantage
1. Initial bias is introduced several time

 One long run
Advantages
1. Less initial bias
2. No restarts
Disadvantages
1. Sample size of 1
2. Difficult to get a good estimate of the variance


1) What is a model?
Answer
This is a logical or physical representation of a complex entity,
system, phenomena or process

2) What is a simulation?
Answer
Simulation is an imitation a of complex entity, system, phenomena or
process meant to functionally reproduce the behaviour of that entity
or process often through the employment of one or more models over
time.

3) Is modeling and simulation one and the same thing? Please give
reasons for your answer.
Answer
No. Modeling can be done without simulation, however, simulation
cannot be done without modeling.

4) In what ways can a system be modeled ?
Answer
o Physically
o Logically
o Analytically (mathematically)

5)Outline the activities involved in the modeling and simulation
lifecycle
Answer
 Define problem
 Build models
 Execute simulation
 Analyze results
 Make decisions
 Validation

Okoro Ugochukwu

Sponsor Documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close