Our Questions and Answers

8/12/2019 Our Questions and Answers

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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.

Startup

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.


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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


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changes to n+1. This is called a Birth. Similarly the departure of jobs changes the system

state to n1. 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


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AMUDA, Tosin Joseph090805009CSC 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$Step 2 .a!idate ode! ass) ptionsStep / Co pare the ode! inp)t-o)tp)t transfor ations to correspondin' inp)t-o)tp)t transfor ationsfor the rea! s$ste

Question 2: hen is a ode! said to ha"e hi'h face "a!idit$1

AnswerA ode! that has high face validity is a ode! that appears to #e a reasona#!e i itation of a rea!-*or!ds$ste to peop!e *ho are no*!ed'ea#!e of the rea! *or!d s$ste &ace "a!idit$ is tested #$ ha"in')sers and peop!e no*!ed'ea#!e *ith the s$ste e3a ine ode! o)tp)t for reasona#!eness and in theprocess identif$ deficiencies An added ad"anta'e of ha"in' the )sers in"o!"ed in "a!idation is that the

ode! s credi#i!it$ to the )sers and the )ser s 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$ &ore3a p!e, if a si )!ation of a fast food resta)rant dri"e thro)'h *as r)n t*ice *ith c)sto er 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 ti e or a3i ) n) #erof c)sto ers *aitin' *o)!d #e e3pected to increase *ith the arri"a! rate

Question 3: 3p!ain the ter s .a!idation and .erification

Answer

.erification is deter inin' that a si )!ation co p)ter pro'ra perfor s as intended, i e , de#)''in'the co p)ter pro'ra 6t deter ines if *e ha"e a #)i!t a si )!ation ode! correct!$

.a!idation is concerned *ith deter inin' *hether the concept)a! si )!ation ode! 7as opposed to theco p)ter pro'ra is an acc)rate representation of the s$ste )nder st)d$ 6t deter ines if *e ha"e#)i!t a correct ode!


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Question 4: hat do $o) )nderstand #$ str)ct)ra! ass) ption and *h$ sho)!d iit #e "a!idated1Answer tructural 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 e3a p!e, the str)ct)re of a )e)in' s$ste ( *hether it is a first co e first ser"e )e)eor a priorit$ )e)e

Man$ str)ct)ra! pro#!e s in the ode! co e fro poor or incorrect ass) ptions To a"oid str)ct)ra!pro#!e s in a ode!, the str)ct)ra! ass) ptions )st #e "a!idated 6f possi#!e the *or in's of the

act)a! s$ste sho)!d #e c!ose!$ o#ser"ed to )nderstand ho* it operates The s$ste s str)ct)re andoperation sho)!d a!so #e "erified *ith )sers of the act)a! s$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 specification , "a!idation : did $o) #)i!d the ri'ht thin' 7tore )ire ents

.erification is done on the co p)teri;ed ode! *hi!e "a!idation is done on the concept)a! ode!

.erification in"o!"es )sin' soft*are en'ineerin' )a!it$ ass)rance techni )es *hi!e "a!idation a es)se of o#


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Questions and Answer

1. Briefly explain the six characteristics of a single server system provided by KendallAnswer: 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/ then 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 tounavailability 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.


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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 queueAnswer: 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 , and the service rate of the server(s), . These are combined into a single

parameter which characterizes a single or multiple server system, the traffic intensity.

Traffic intensity, =

4. State Littles lawAnswer: Littles 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 anitem 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

= average number of items arriving per unit time, the law is

L = W

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,


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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.


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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.


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5. Discuss the parameters to consider in queuing analysis.

Answer Population size Number of servers System capacity Arrival process Service time distribution Service discipline


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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 mean 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


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NAME: OPEOLUWA JOSEPH 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


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b. . Job 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.


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2. Differentiate between a deterministic and stochastic Model

A deterministic Model is a model which, given a particular input, will alwaysproduce 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 theform 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 implementationaccurately represents the developers conceptual description of the model andthe solution to the model.

Validation is the process of determining the degree to which a model is anaccurate representation of the real world from the perspective of the intendeduses of the model.


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5. What is discrete event simulation

Discrete event simulation (DES) is the process of modelling the behavior of acomplex system (mathematically or logically) as an ordered sequence of well-

defined events. In this context, an event comprises a specific change in thesystem's state at a specific point in time.


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NAME: OYEWOLE, Mopelola O.

MATRIC NO.: 090805054

DEPARTMENT: COMPUTER SCIENCES

COURSE: CSC 524

ASSIGNMENT: QUESTIONS ONPERFORMANCE MODELING

LECTURER: ADEWOLE, A. P. (DR.)


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Questions

1a. What is performance modeling?

Answer

Performance modeling is a structured and repeatable approach to modeling the performanceof 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 knowwhether 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.


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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.


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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 resourcecosts 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?

AnswerThe 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 DocumentThe performance model document may contain the following:


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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 BudgetIdentify 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).


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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.


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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 toanalyse individual resources in computer systems. The central element of the system is aserver, 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 queuearrive at another queue (or possibly the same queue).

3. In an Open queueing Network, it has both external arrivals and departures where jobs enterthe system at In and exit at Out. In a Closed queueing Network, It has no externalarrivals or departures but the jobs in the system keep circulating from one queue to thenext. 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 forinteractive 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 (andnot as a batch). The state of such a system can be represented by the number of jobs n inthe 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 numberof 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 anddispatching discipline of the queue.


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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 conceptualmodel 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 thereare no errors in simulation. Verification asks the question: are we building the modelright?

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 ValidationFace Validation is a process where experts of the problem entity domain check theconceptual 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 levelprogramming languages (e.g Java) for model implementation, Why?

The use of Simulation language will result in easier implementation, reduceprogramming 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, asthe predictive accuracy of the model.


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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?


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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)


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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 andnon-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 conditionthat is part of the model e.g. a bank with an opening time of 8.am and closing time of5pm. 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 ncustomers, machine utilization, work-in-progress etc). Confidence interval forterminating simulations usually uses independent replications.

While a steady state simulation is one where there is no specific starting and endingconditions e.g. an emergency room. Here we are interested in the steady state behavior ofthe 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 simulationsstate variables. The choice of initial conditions can have a significant but unrecognizedimpact on the simulation runs outcome.

How long must it run before data representative of steady state can be collectedThe 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 biasDetecting:

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


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retained for analysis. Experimenter hopes that the remaining data arerepresentative 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 manyshort 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 statemean.

One long runMake just one long replication so that the initial bias is only introduced once. Thisway, you will not be throwing out a lot of data. The problem here is how youestimate 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

Advantages1. Simple analysis, similar to the analysis of terminating systems2. The data from different replications are independent and identically

distributedDisadvantage

1. Initial bias is introduced several time

One long runAdvantages1. Less initial bias2. No restartsDisadvantages1. Sample size of 12. Difficult to get a good estimate of the variance


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

2) What is a simulation? AnswerSimulation is an imitation a of complex entity, system, phenomena orprocess meant to functionally reproduce the behaviour of that entityor process often through the employment of one or more models overtime.

3) Is modeling and simulation one and the same thing? Please givereasons for your answer.

AnswerNo. Modeling can be done without simulation, however, simulationcannot be done without modeling.

4) In what ways can a system be modeled ? Answero Physicallyo Logicallyo Analytically (mathematically)

5)Outline the activities involved in the modeling and simulationlifecycle

Answer Define problem Build models Execute simulation Analyze results Make decisions Validation

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