Simulation on Single Server & Distributed Environment (It’s Comparison & Issues)

doi:10.4236/wjet.2013.12004

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World Journal of Engineering and Technology, 2013, 1, 23-25

http://dx.doi.org/10.4236/wjet.2013.12004 Published Online August 2013 (http://www.scirp.org/journal/wjet)

Simulation on Single Server & Distributed Environment

(It’s Comparison & Issues)

A. Jawwad Memon1, Wasi Ur Rehman2

1Department of Computer Science, Institute of Business & Technology (IBT), Karachi, Pakistan; 2Department of Computer Science,

Institute of Business & Technology (IBT), Karachi, Pakistan.

Email: [email protected]

Received June 3rd, 2013; revised July 6th, 2013; accepted August 2nd, 2013

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

properly cited.

ABSTRACT

Simulation has become the evaluation method of choice for many areas of distributing computing research. Simulation

has been applied successfully for modeling small and large complex systems and understanding their behavior, espe-

cially in the area of distributed systems or parallel environment. The aim of my research is to study and qualitative

analysis of simulation on a single server & on distributed environment and finding the related issues & its comparison.

Keywords: Simulation; Simulation on Single Server Environment; Simulation on Distributed Environment; Simulation

Issues

1. Introduction

The terms “simulation” and “modeling” are sometimes

used alternatively. In reality, they are distinct, though

related, terms.

Simulation means mimicking of real life or potential

situations, usually using computers. It is the imitative

representation of the functioning of one system or proc-

ess by means of the functioning of another, such as a

computer simulation of an industrial process. With simu-

lation, one can examine a problem that is often not sub-

ject to direct experimentation.

1.1. Basic Simulation Model

Basic simulation model structure is shown in Figure 1.

Simulation is the art of using tools—physical or con-

ceptual models, or computer hardware and software, to

attempt to create the illusion of reality. The discipline has

in recent years expanded to include the modeling of sys-

tems that rely on human factors and therefore possess a

large proportion of uncertainty, such as social, economic

or commercial systems.

Simulation is the technique of using some tools—ei-

ther physical or conceptual models, or based on computer

Experiment with the actual systemExperiment with a model of the system

Physical ModelMathematical Model

Analytical ModelSimulation

System

Figure 1. Basic simulation model structure [1].

Simulation on Single Server & Distributed Environment (It’s Comparison & Issues)

hardware and software; try to create the virtual illusion of

real objects [2].

1.2. Simulation Purpose: When to Use

 The study of experiments: Simulation enables with

internal interactions.

 Knowledge gained from simulations very useful in

order to improve the system.

 Simulations can be used to verify analytical results,

e.g. queuing systems [3].

2. Literature Review

Applications, Models, Simulation & Design Models are

shown in Figure 2.

2.1. Simulation on Single Server Environment

Simulation on a single server environment is based on

client server architecture where its work on single pc

environment.

In the area of IT, client-server models exhibit a degree

of complexity and richness not amenable to easy ana-

lytical solutions, except for some specific algorithms

useful in limited contexts. Simulation could, therefore, be

a good strategy to analyze the client-server systems and

help in better implementation of feasible solutions [4].

2.2. Simulation on Distributed Environment

Any simulation in which the number of processors is

more than 1 involved [5].

Distributed systems typically consist of a large number

of actors that act and interact with each other in a highly

dynamic or changing environment [6].

Design and development of distributed system is

widespread in discrete-event simulation, for example: it

is use to understand network protocols [7].

Performance and functionality of complex inter com-

ponent protocol and algorithm is defined by simulation,

these functions and algorithms written in different pro-

gramming languages.

Simulation gives an opportunity to developer to cap-

ture basic functionality at the same time as they are

working on topology, bandwidth, timing and overall pro-

perties of distributed system. The code that implements a

simulation distributed system is formal speciation of the

intended functional behavior of that system, whose be-

havior is parameterized by a well-defined set of control-

lable distribution properties in addition to normal inputs

[8].

According to this observation, simulation can be used

within a specification based testing to provide developers

of distributed system with a method for selective effec-

tive test suites. This analysis give advantage of specifica-

tion is executable to program code, in concern of distrib-

uted system and the simulation code is correct specifica-

tion.

2.3. Why Use Simulation

 It makes the simulation process faster with large

number of processors.

 It simulate larger amount of data with greater memory

& resources.

 It integrates geographically distributed simulators [9].

3. Related Work

3.1. Simulation on Single Server Environment

Processing performed in single server environment is

shown on Figure 3. It clearly shows simulation on a sin-

gle server environment in which we have single processor.

3.2. Simulation on Distributed Environment

Processing performed in distributed environment is

shown in Figure 4. It clearly shows simulation on a dis-

tributed environment in which we have multiple proces-

sors.

Application

Areas

Parallel and Distributed

Simulation &

Design Models

Models

Simulation

Model

Industrial

Processes

Environmental

Resources

Many other

applications

…

Design

Model

Discrete

event

Continuous

event

Visual-

based

Library-

based

Hibrid

(discrete and

continuous)

Systems

Figure 2. Applications, models, simulation & design models [6].

Simulation on Single Server & Distributed Environment (It’s Comparison & Issues) 25

Sequential

1 processors

Example:

Figure 3. Processing performed in single server environment.

arallel

> 1 processors

xample:

processors

Figure 4. Processing performed in distributed environment.

3.3. Comparison: Simulation in Single Server

Environment

 Simulation performed on single server.

 It has geographic limitation due to single server.

 Single processor use.

 It makes the simulation process slower because single

core processor use in simulation.

 The cost of setup is comparatively low.

 To maintain one server is easy as compared to multi-

ple servers.

3.4. Comparison: Simulation in Distributed

Environment

 Simulation performed on distributed servers.

 It integrates geographically distributed simulators.

 Multiple processor use.

 It makes the simulation process faster because single

core processor use in simulation

 The cost of is high because multiple processors used.

 It’s quite difficult job to maintain huge no. of servers

on distributed environment.

4. Conclusion

Simulation has been applied successfully for modeling

small and large complex systems. Simulation is the art to

create a physical and conceptual model which can repre-

sent a system or create the illusion of reality. Simulation

helps to make experiment for understanding the behavior

of system. Computer simulation gives opportunity to

observe a real world experience and interact with it. As

we all know that purchasing physical equipment for

every experiment is almost not possible and required a

large amount of funding.

REFERENCES

[1] M. Güneş, “Figure 1 Basic Simulation Model—Modeling

and Performance Analysis with Discrete-Event Simula-

tiongy,” Computer Science, Informatik 4 Communication

and Distributed Systems, Chapter 1.

[2] S. Raczynski, “Modeling and Simulation: The Computer

Science of Illusion,” 1st Edition, 2006.

[3] M. Güneş, “Modeling and Performance Analysis with

Discrete-Event Simulationgy,” Computer Science, Infor-

matik 4 Communication and Distributed Systems, Chap-

ter 1.

[4] Y. L. Deshpande, “Roger Jenkins, & Simon Taylor,” Use

of Simulation to Test Client-Server Models, pp. 1210-

1217.

[5] K. Perumalla, “Parallel and Distributed Simulation

(PADS): Traditional Techniques & Recent Advances,”

Oak Ridge National Laboratory, 2007.

[6] F. Calzolai and M. Loreti, “Simulation and Analysis of

Distributed Systems in Klaim,” Proceedings of the 12th

International Conference on Coordination Models and

Languages, pp. 122-136.

doi:10.1007/978-3-642-13414-2_9

[7] M. Allman and A. Falk, “On the Effective Evaluation of

TCP,” ACM Computer Communication Review, Vol. 29,

No. 5, 1999, pp. 59-70. doi:10.1145/505696.505703

[8] M. J. Rutherford, A. Carzaniga, Er. L. Wolf, C. Matthew

and J. Rutherford, “Simulation-Based Testing of Distrib-

uted Systems,” SiteseerX Technical Report CU-CS-

1004-06, 2006.

[9] K. Perumalla, “Parallel and Distributed Simulation

(PADS): Traditional Techniques &Recent Advances,”

Oak Ridge National Laboratory, 2007.