Modeling methods in OPNET simulations of Tactical Command and ...
Modeling methods in OPNET simulations of Tactical Command and ...
Modeling methods in OPNET simulations of Tactical Command and ...
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<strong>Model<strong>in</strong>g</strong> <strong>methods</strong> <strong>in</strong> <strong>OPNET</strong> <strong>simulations</strong> <strong>of</strong> <strong>Tactical</strong> <strong>Comm<strong>and</strong></strong> <strong>and</strong> Control<br />
Information Systems<br />
J. Mohorko, M. Fras, Ž. Čučej<br />
Laboratory for Signal Process<strong>in</strong>g <strong>and</strong> Remote Control<br />
University <strong>of</strong> Maribor, Faculty <strong>of</strong> Electrical Eng<strong>in</strong>eer<strong>in</strong>g <strong>and</strong> Computer Science<br />
Smetanova ul. 17, SI-2000 Maribor, Slovenia<br />
Phone: +386 2 22071840 Fax: +386 2 2207272 E-mail: mohorko@uni-mb.si<br />
Keywords: MIP, C2IEDM, C2IS, Replication, <strong>OPNET</strong>, <strong>Tactical</strong> networks, Traffic model<strong>in</strong>g, Simulations, WLAN<br />
Abstract - Slovenia is a member <strong>of</strong> MIP (Multilateral<br />
Interoperability Programme), the task <strong>of</strong> which is to provide<br />
<strong>in</strong>terpretations <strong>of</strong> national C2IS systems for successfully<br />
harmoniz<strong>in</strong>g jo<strong>in</strong>t action by <strong>in</strong>ternational military<br />
peacekeep<strong>in</strong>g forces. Interpretability with<strong>in</strong> MIP is carriedout<br />
by a unified model based on controlled data replication<br />
between the databases <strong>of</strong> C2IS systems. Systematic IRIS<br />
Replication Mechanism s<strong>of</strong>tware (IRM) is used for data<br />
replication. This paper presents <strong>methods</strong> <strong>of</strong> measur<strong>in</strong>g <strong>and</strong><br />
analyz<strong>in</strong>g traffic that causes IRM. We designed an <strong>OPNET</strong><br />
model on the basis <strong>of</strong> the analyzed results that would be<br />
suitable for the purposes <strong>of</strong> tactical radio network simulation.<br />
1. INTRODUCTION<br />
Simulations by the model<strong>in</strong>g <strong>and</strong> analyz<strong>in</strong>g <strong>of</strong><br />
communication system’s characteristics, has become one<br />
<strong>of</strong> the ma<strong>in</strong> challenges when construct<strong>in</strong>g networks, <strong>and</strong><br />
the development <strong>of</strong> network devices <strong>and</strong> protocols. The<br />
Slovenian army also felt such needs, when it called for a<br />
national research project 1 , the object which was the<br />
optimization <strong>of</strong> tactical radio communication networks.<br />
This would represent <strong>in</strong>frastructure for a national <strong>Tactical</strong><br />
<strong>Comm<strong>and</strong></strong> <strong>and</strong> Control Information System - C2IS<br />
(TISPINK <strong>in</strong> the Slovene language). Slovenian TISPINK<br />
is a member <strong>of</strong> the Multilateral Interoperability<br />
Programme (MIP), the mission <strong>of</strong> which is to ensure the<br />
<strong>in</strong>terpretability <strong>of</strong> national C2IS systems for harmoniz<strong>in</strong>g<br />
the activities <strong>of</strong> <strong>in</strong>ternational forces <strong>in</strong> jo<strong>in</strong>t peacekeep<strong>in</strong>g<br />
operations. Connectivity with<strong>in</strong> MIP enables a unified<br />
<strong>Comm<strong>and</strong></strong> <strong>and</strong> Control Information Exchange Data Model<br />
(C2IEDM), which is based a controlled replication <strong>of</strong> data<br />
between the C2IS system’s databases. The s<strong>of</strong>tware<br />
environment <strong>of</strong> Slovenian TISPINK is based two products<br />
from the Danish s<strong>of</strong>tware producer Systematic; these are<br />
Sitaware <strong>and</strong> IRM (IRIS Repliacation Mechanism).<br />
Sitaware represents graphic <strong>in</strong>terface for: the entry <strong>of</strong> data<br />
<strong>in</strong> to the C2IS database, GIS, <strong>and</strong> the plann<strong>in</strong>g <strong>and</strong> analysis<br />
<strong>of</strong> tactical operations <strong>in</strong> the field. IRM enables a controlled<br />
exchange <strong>of</strong> data between tactical units on the battlefield<br />
[1, 2, 3 ].<br />
<strong>OPNET</strong> is one <strong>of</strong> most powerful simulation tools for the<br />
analysis, plann<strong>in</strong>g <strong>and</strong> optimization <strong>of</strong> communication<br />
networks, devices <strong>and</strong> protocols. The best known use <strong>of</strong><br />
<strong>OPNET</strong> <strong>in</strong> the defense area orig<strong>in</strong>ates through the<br />
NETWARS programme <strong>of</strong> the US Department <strong>of</strong> Defense<br />
[5, 6,7].<br />
1 Target research programmes "Science for Peace <strong>and</strong> Security”:<br />
M2-0140 - <strong>Model<strong>in</strong>g</strong> <strong>of</strong> <strong>Comm<strong>and</strong></strong> <strong>and</strong> Control <strong>in</strong>formation<br />
systems, f<strong>in</strong>anced by Slovenian M<strong>in</strong>istry for Defense.<br />
This paper presents a segment <strong>of</strong> the outcome regard<strong>in</strong>g<br />
the research <strong>of</strong> our project. The goal is to develop <strong>methods</strong><br />
for the model<strong>in</strong>g <strong>of</strong> data traffic <strong>in</strong> a tactical network that is<br />
the consequence <strong>of</strong> the IRIS replication mechanism’s<br />
activity. The 2nd section briefly <strong>in</strong>troduces the <strong>OPNET</strong><br />
simulation package <strong>and</strong> its capabilities for evaluat<strong>in</strong>g<br />
telecommunication systems. Section 3 follows, which<br />
describes how to build a laboratorial model <strong>of</strong> the<br />
TISPINK system, where radio connections are replaced by<br />
Ethernet network. Ethernet traffic, captured by us<strong>in</strong>g the<br />
Ethereal network protocol analyzer, was analyzed us<strong>in</strong>g an<br />
ACE (Application Characterization Environment) module<br />
<strong>of</strong> <strong>OPNET</strong>. In section 4 tests <strong>and</strong> evaluates some <strong>of</strong> the<br />
<strong>OPNET</strong> traffic model<strong>in</strong>g <strong>methods</strong> suitable for our case.<br />
The results are summarized <strong>in</strong> section 5.<br />
2. <strong>OPNET</strong> OVERWIEW<br />
<strong>OPNET</strong> is a lead<strong>in</strong>g tool for simulat<strong>in</strong>g <strong>and</strong> evaluat<strong>in</strong>g<br />
telecommunication systems. It was presented for the first<br />
time to the communication <strong>in</strong>dustry <strong>in</strong> 1986.<br />
Fig. 1: <strong>OPNET</strong> with Project Editor, Node Editor <strong>and</strong> C-Source<br />
code Editor.
The basis is on object-oriented simulation approach<br />
supported by a series <strong>of</strong> graphic user <strong>in</strong>terface - editors<br />
(see Fig.1), which rather simplify the process <strong>of</strong><br />
communication networks model<strong>in</strong>g, devices, <strong>and</strong><br />
protocols. The project Editor enables the entry <strong>of</strong> graphic<br />
description <strong>of</strong> network topology. The Node Editor is used<br />
for describ<strong>in</strong>g protocols, <strong>and</strong> the connections between<br />
them, by us<strong>in</strong>g layers <strong>of</strong> the ISO/OSI model for<br />
communication devices. The Process Editor is an<br />
extension <strong>of</strong> programm<strong>in</strong>g language C. It uses a powerful<br />
f<strong>in</strong>ite-state mach<strong>in</strong>e (FSM) approach to represent <strong>of</strong><br />
different communication algorithms <strong>and</strong> protocols. The<br />
activity <strong>of</strong> <strong>in</strong>dividual state <strong>in</strong> FSM is implemented by<br />
programm<strong>in</strong>g language C. <strong>OPNET</strong> <strong>in</strong>corporates a lot <strong>of</strong><br />
already-prepared simulation models <strong>of</strong> st<strong>and</strong>ard<br />
communication equipment <strong>and</strong> protocols for wired, radio<br />
<strong>and</strong> optical transmission mediums. [4].<br />
2.1 <strong>OPNET</strong> module ACE<br />
<strong>OPNET</strong> is designed modularly. One <strong>of</strong> modules, used <strong>in</strong><br />
this study case, is ACE (Application Characterization<br />
Environment) [4]. ACE is a tool for the visualization,<br />
analysis <strong>and</strong> prediction <strong>of</strong> traffic <strong>in</strong> network applications.<br />
It helps us with the activity analysis <strong>of</strong> exist<strong>in</strong>g, <strong>and</strong> the<br />
development <strong>of</strong> new applications. This module allows the<br />
import<strong>in</strong>g <strong>of</strong> captured traffic by sniffer <strong>and</strong> analysis for the<br />
follow<strong>in</strong>g <strong>in</strong>tentions:<br />
• Diagnos<strong>in</strong>g application problems<br />
• Predict<strong>in</strong>g application behavior<br />
2.2 <strong>Model<strong>in</strong>g</strong> <strong>of</strong> network traffic by <strong>OPNET</strong><br />
The <strong>OPNET</strong> modeler allows for two basic <strong>methods</strong> <strong>of</strong><br />
network traffic model<strong>in</strong>g: explicit traffic <strong>and</strong> background<br />
traffic. For Explicit traffic, <strong>OPNET</strong> produces every packet<br />
separately. This method <strong>of</strong> network traffic generation also<br />
calls packet by packet traffic. In this manner the traffic <strong>of</strong><br />
the real system is clearly imitated, however such<br />
simulation is more numerically dem<strong>and</strong><strong>in</strong>g. These three<br />
<strong>methods</strong> for model<strong>in</strong>g explicit traffic:<br />
• Packets generator by which model<strong>in</strong>g on the<br />
network node determ<strong>in</strong>es the streams <strong>of</strong> packets<br />
exactly.<br />
• Application dem<strong>and</strong>s assign traffic between two<br />
network nodes.<br />
• Applications traffic model where traffic is generated<br />
by st<strong>and</strong>ard application models.<br />
Background traffic is analytically modeled traffic,<br />
which has an <strong>in</strong>fluence on the properties <strong>of</strong> a system <strong>in</strong> the<br />
form <strong>of</strong> additional delay. Background traffic can also be<br />
used <strong>in</strong> comb<strong>in</strong>ation with explicit traffic. <strong>OPNET</strong> allows<br />
the follow<strong>in</strong>g <strong>methods</strong> for model<strong>in</strong>g background traffic:<br />
• Traffic flow describes f<strong>in</strong>ite end-to-end traffic from<br />
source node to dest<strong>in</strong>ation node.<br />
• Basel<strong>in</strong>e loads represent background traffic on one<br />
<strong>of</strong> the selected l<strong>in</strong>ks (or from the node).<br />
• Application dem<strong>and</strong>s can also be used to represent<br />
background traffic between two nodes.<br />
(represent<strong>in</strong>g tactical units), as shown <strong>in</strong> Figure 2. C2IS<br />
database, Sitaware user <strong>in</strong>terface <strong>and</strong> IRIS replication<br />
mechanism are <strong>in</strong>stalled on every station. All stations are<br />
connected, over hub, <strong>in</strong> Ethernet network, which replaces<br />
radio network. The network is, on IP level, distributed<br />
over two subnets: broadcast <strong>and</strong> peer-to-peer. Stations <strong>in</strong><br />
subnets are def<strong>in</strong>ed us<strong>in</strong>g IP addresses, as shown <strong>in</strong> Table<br />
1. Another computer with <strong>in</strong>stalled s<strong>of</strong>tware Ethereal for<br />
captur<strong>in</strong>g Ethernet traffic (sniffer) was added to this rest<br />
network.<br />
Fig. 2: Block scheme <strong>of</strong> TISPINK test system.<br />
Unit IP address Peer-topeer<br />
Broadcast<br />
1.Brigade 192.168.1.1 x<br />
20.Battalion 192.168.20.1 x<br />
10.Battalion 192.168.10.1 x x<br />
1. Company 192.168.10.11 x<br />
2. Company 192.168.10.12 x<br />
Table 1: Logical distribution <strong>of</strong> units <strong>in</strong> test systems TISPINK<br />
with two subnets Peer-to-peer <strong>in</strong> Broadcast<br />
Data traffic, between stations <strong>in</strong> test network, is def<strong>in</strong>ed<br />
by contracts which are established by the IRIS replication<br />
mechanism. A contract def<strong>in</strong>es data contents <strong>and</strong><br />
dest<strong>in</strong>ations, where the data will be transmitted. Data<br />
contents are modeled by data sources (GPS, VoIP, manual<br />
entry, electronic mail, etc), which cause these contents.<br />
Contracts were divide by consider<strong>in</strong>g the dest<strong>in</strong>ations,<br />
which are addressed on certa<strong>in</strong> unit (peer-to-peer) <strong>and</strong><br />
contracts, which are addressed on all units with<strong>in</strong> the same<br />
subnet (broadcast).<br />
3. MEASURING AND ANALYSIS OF TISPINK<br />
TRAFFIC IN <strong>OPNET</strong><br />
A test laboratorial model <strong>of</strong> the TISPINK system was<br />
built. This test network is composed <strong>of</strong> five workstations<br />
Fig. 3: Measur<strong>in</strong>g <strong>of</strong> traffic with sniffer Ethereal
Data traffic occurs between <strong>in</strong>dividual units, when the<br />
IRM mechanism detects any change <strong>in</strong> the database. If<br />
there is a contract that refers to changed data, then this data<br />
will be sent to the dest<strong>in</strong>ation address. With the help <strong>of</strong><br />
SITAWARE GIS user-<strong>in</strong>terface, we moved <strong>of</strong> unit on<br />
fieldwork. The traffic caused by movement is measured<br />
us<strong>in</strong>g the Ethernet sniffer, as shown <strong>in</strong> Figure 3.<br />
Measur<strong>in</strong>g acquires the follow<strong>in</strong>g <strong>in</strong>formation about<br />
network traffic: time <strong>of</strong> generat<strong>in</strong>g packets, size <strong>of</strong> packets,<br />
type <strong>of</strong> protocol, <strong>in</strong>ter-arrival time, source IP, <strong>and</strong> the<br />
dest<strong>in</strong>ation IP address.<br />
analyses, <strong>of</strong> captured traffic, we can use mathematical<br />
fitt<strong>in</strong>g tools such as EasyFit, as shown <strong>in</strong> Figure 6.<br />
With the help <strong>of</strong> the described tools, we can analyze<br />
measured traffic, which was caused by different activity<br />
conditions (broadcast <strong>and</strong> peer-to-peer) for IRM<br />
application <strong>in</strong> the tactical test network.<br />
Fig. 6: Tool for choos<strong>in</strong>g the distribution function <strong>and</strong> its<br />
parameters.<br />
4. SIMULATION RESULTS<br />
Fig. 4: Entry measure (captured) <strong>of</strong> traffic <strong>in</strong> the ACE module<br />
Us<strong>in</strong>g the <strong>OPNET</strong> ACE module the impact <strong>of</strong> different<br />
parameters was analyzed such as, for <strong>in</strong>stance, the<br />
b<strong>and</strong>width on delay <strong>in</strong> the network. First the captured<br />
traffic <strong>in</strong> the Ace Module was imported. Then the ACE<br />
module created a hierarchical picture <strong>of</strong> the network, as<br />
shown <strong>in</strong> Figure 4, where data connections between<br />
participants <strong>in</strong> tactical networks can also be seen. Stations<br />
<strong>in</strong> the network were named by IP addresses. The station <strong>in</strong><br />
Figure 4 with the IP number 192.168.10.255 is virtual<br />
station, which <strong>in</strong>troduces the broadcast IP address.<br />
The Data Exchange chart is used for analysis <strong>of</strong> traffic<br />
<strong>and</strong> packets <strong>of</strong> captured traffic, <strong>and</strong> <strong>of</strong>fers the exam<strong>in</strong>ation<br />
<strong>of</strong> time-dependence execution regard<strong>in</strong>g protocol between<br />
participant transmitt<strong>in</strong>g stations. In the case <strong>of</strong> such a<br />
graph with enlarged detail is shown Figure 5.<br />
<strong>OPNET</strong> allows the model<strong>in</strong>g <strong>of</strong> two types <strong>of</strong> traffic:<br />
explicit traffic <strong>and</strong> background traffic. In the explicit case,<br />
the traffic is modeled packet by packet. This is opposite to<br />
background traffic, which is analytically described <strong>and</strong> has<br />
an effect on network performance with additional delays.<br />
The analysis <strong>of</strong> traffic <strong>in</strong> the previous section enables the<br />
study<strong>in</strong>g <strong>of</strong> different possibilities for traffic model<strong>in</strong>g by<br />
<strong>OPNET</strong> <strong>simulations</strong>.<br />
The traffic <strong>of</strong> application IRM will be model<strong>in</strong>g us<strong>in</strong>g<br />
explicit traffic, because <strong>of</strong> the dem<strong>and</strong>ed reliability. Three<br />
different <strong>methods</strong> <strong>of</strong> model<strong>in</strong>g exist: a traffic generator,<br />
application dem<strong>and</strong>s, <strong>and</strong> an application traffic model.<br />
4.1 Applications traffic model<br />
<strong>OPNET</strong> conta<strong>in</strong>s 16 st<strong>and</strong>ard-application traffic models<br />
(as shown <strong>in</strong> Figure 7), which can be used on the<br />
follow<strong>in</strong>g node models (stations): WLAN workstation,<br />
MANET workstation <strong>and</strong> Ethernet workstation.<br />
Fig. 5: More detailed display <strong>of</strong> one movement <strong>of</strong> the unit <strong>and</strong><br />
generated packets <strong>of</strong> IRM application<br />
In <strong>OPNET</strong> we <strong>of</strong>ten model traffic, which is def<strong>in</strong>ed<br />
statistically with probability distributions for bit rate <strong>and</strong><br />
time between packets (<strong>in</strong>ter arrival time). For such<br />
Fig.7: St<strong>and</strong>ard applications <strong>in</strong> <strong>OPNET</strong>.
Parameters can be changed for every model <strong>of</strong><br />
application <strong>in</strong> regard to: size <strong>of</strong> packets, amount <strong>of</strong> packets<br />
(data rate), transport protocol, number <strong>of</strong> repeated send<strong>in</strong>g,<br />
timeouts, retransmissions, failure <strong>and</strong> recovery, etc. In<br />
addition, to us<strong>in</strong>g st<strong>and</strong>ard applications we can also model<br />
new 'custom' applications. We modeled a custom<br />
application with the help <strong>of</strong> the results derived from the<br />
ACE analysis described <strong>in</strong> the previous section.<br />
4.2 Application dem<strong>and</strong>s<br />
Applications dem<strong>and</strong> is a way <strong>of</strong> traffic model<strong>in</strong>g, where<br />
the application’s behavior is modeled. Dem<strong>and</strong>s<br />
characterize traffic by the sizes <strong>and</strong> rates <strong>of</strong> the requests<br />
<strong>and</strong> responses, between two nodes <strong>in</strong> both directions.<br />
considered criteria we decided that the traffic <strong>of</strong> IRM<br />
application would be modeled us<strong>in</strong>g traffic generators on a<br />
MANET station. This traffic generator does not have the<br />
possibility <strong>of</strong> def<strong>in</strong><strong>in</strong>g statistics for the ON/OFF process,<br />
which is a disadvantage <strong>of</strong> the traffic generator. This is the<br />
reasons why we were forced to change the <strong>in</strong>terpretation <strong>of</strong><br />
a traffic generator’s activity mechanism. In the new<br />
<strong>in</strong>terpretation we treat one packet as one transaction <strong>of</strong> the<br />
IRIS replication mechanism, where the new size <strong>of</strong> the<br />
packet is the sum <strong>of</strong> the transaction’s packets. We <strong>in</strong>terpret<br />
time between packets as time between <strong>in</strong>dividual<br />
transactions. Every MANET station enables the<br />
simultaneously activity <strong>of</strong> arbitrarily number <strong>of</strong> the traffic<br />
generators. For each <strong>of</strong> them, we can def<strong>in</strong>e dest<strong>in</strong>ation<br />
address (peer-to-peer or broadcast) <strong>and</strong> statistical<br />
descriptions <strong>of</strong> packet size <strong>and</strong> <strong>in</strong>ter-arrival time.<br />
Fig. 8: <strong>Model<strong>in</strong>g</strong> applications dem<strong>and</strong> between station <strong>and</strong><br />
server.<br />
4.3 Traffic generator<br />
We can model network traffic by a traffic generator,<br />
where the traffic is described statistically by packet size<br />
<strong>and</strong> time between packets (<strong>in</strong>ter arrival time). There are<br />
approximately 20 different probability distributions<br />
available.<br />
Traffic generators <strong>in</strong> <strong>OPNET</strong> are conta<strong>in</strong>ed <strong>in</strong> the<br />
follow<strong>in</strong>g node models (stations): Ethernet IP station, PPP<br />
IP station, MANET station. In some models (WLAN<br />
station <strong>in</strong> Figure 9) beside these two parameters, there is<br />
also the possibility <strong>of</strong> model<strong>in</strong>g the distribution <strong>of</strong><br />
probability for active <strong>and</strong> none-active states <strong>of</strong> the node<br />
(ON state <strong>and</strong> OFF state).<br />
5. CONCLUSION<br />
We modeled traffic <strong>of</strong> the IRM application <strong>in</strong> three<br />
different ways. Every method has certa<strong>in</strong> advantages <strong>and</strong><br />
disadvantages <strong>in</strong> its use. By choice, most suitable <strong>methods</strong><br />
consider the follow<strong>in</strong>g criteria: wireless station, mobility,<br />
consideration <strong>of</strong> terra<strong>in</strong> <strong>in</strong> the communication model,<br />
possibility <strong>of</strong> sett<strong>in</strong>g wireless LAN parameters for the<br />
model<strong>in</strong>g <strong>of</strong> radio devices, possibility <strong>of</strong> communication <strong>in</strong><br />
broadcast <strong>and</strong> peer-to-peer protocol. On the basis <strong>of</strong> the<br />
REFERENCES<br />
Fig. 9: <strong>Model<strong>in</strong>g</strong> <strong>of</strong> traffic by WLAN station<br />
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