Communication Networks –Modeling, Simulation & Emulation

Jim Nutaro

Computational Sciences and Engineering Division

Goal - Modeling of Large Scale, Complex System ofSystems that are Dependent on Information Flow

• Future Combat System

– ORNL part of FIST team.

• Real-time Cyber SecuritySituational Awareness

– Joint USSTRATCOM-DISAEffort for DOD.

– ORNL lead player forUSSTRATCOM.

• Critical InfrastructureNetwork

– Example: The Electric PowerGrid

– ORNL DHS National Lab incharge of TVTA for cyber.

• Sensor Networks

• Information Operations

Metropolitan Area

Network (MAN)Integrated Space and Terrestrial Wide Area Network

MAN

Assured

Net Cached Data Regional and Globally

Distributed Processing

Collaboration

Services

Secure IP Network

Convergence Layer

Battlespace Reach back

and Smart Pull61 el. T

heater

8GHz MBR

169 el. 7GHz M

BT44

/

20

256 element

TPA484 element

RPA

Data

Data

Data

Data

MANT-SAT

T-SAT

T-SAT

T-SAT

Communications M&S – Overview

Interoperability- Wired, wireless, hybrid simulators

- Simulator-independent models

Applications- Network security

- Testing and analysis

Modeling- Wireless for MOUT

- Modeling frameworks

Visualization- 104-106 nodes, links

- Animation & Sonification

Scalability- Size and speed

- Supercomputing

Interoperable Tools – Example Scenario Ship-to-ship, ship-to-shore, ship-to-stationnetwork

• 7 Ships

• 1 Ground station

• 1 Satellite - 220ms latency, 512kbpspeak shared bandwidth

• Packeteer traffic shaper on each ship

• Ethernet LAN on each ship

• COTS hardware/software on each ship

• Running Windows NT, Solaris

• Lotus Domino, Internet Explorer, …

On-shore &airborne mobilead-hoc networks

Given:

• Afloat Naval Exercise Data (JWID00) atReconfigurable Land-based Test Site (RLBTS)modeled in ns-2

•Ship-to-shore & Shore-to-ObjectiveManeuver (STOM) data modeled in GloMoSim

GloMoSimPDNS

Integrated Tool Configuration

satellite link Line-of-sight link

• Intra-ship LAN traffic - File transfer, web & email proxies, …

• Inter-ship WAN traffic - Position updates, calls for fire, … - Web, email, chat, …

• Cluster traffic - Images, orders, updates, …

• Remote traffic, … - Orders, video streams, …

Ship

Ship Ship

Ship

Mobilenodes

Integration Methodology & Framework

Runtime Infrastructure (RTI)

Integration Services

• Semantic Integration

– Protocol and item registration

– Consensus computation

– Message Export / Import services

• Parallel Execution

– Multicast-based Event distribution

– Synchronization (Time Management)

Parallel/ Distributed

Simulation Protocol

DEVS

Joint

Measure

A platform effectivenessmodeling toolCreated at LockheedMartin.

Models are specified usingthe DEVS formalism.

DEVS is implemented as anobject oriented simulationtoolkit.

Joint MEASURE wasimplemented with DEVS-C++ from the University ofArizona.

Example – DEVS & Support of Joint Measure

xxxCommand

ControlModel

xxxxLaserModel

x

x

x

SpaceBased

Discrimination

x

x

x

SpaceBasedLaser

x

x

x

x

x

x

x

x

x

MissileDefense

(Theater /National)

xxxxxxWaypoint& HeadingNav Model

xxxxOrbital

PropagateModel

x

x

x

Integrated System

Center

x

x

x

CommonAero

Vehicle

x

x

x

x

JointCompositeTrackingNetwork

xxBallistic

TrajectoryModel

xWeatherModel

xxxxEarth &TerrainModel

xxComm.

Model

xMissileModel

xxIR SensorModel

xxxxRadarModel

SpaceOperations

Vehicle

Coast GuardDeep Water

ArsenalShip

GlobalPositioningSystem III

CriticalMobileTarget

Project

Model

Component Model (Actual Reuse) Matrix

Parallel/Distributed Network Simulation

ns-2Sequential

Simulator

ns-2Sequential

Simulator

ns-2Sequential

Simulator

ns-2Sequential

Simulator

Runtime Infrastructure Other

Services

Virtual Time

Synchronization

Asynchronous

Distributed

Reductions

Heterogeneous Network

Communication

Runtime Interface

Application

(GTNetS, ns-2, …)

Popular sequential models (ns-2)parallelized by integrating multipleinstances of itself.

Supercomputer

Network Models

Large-scale Packet-level Simulation

0

20

40

60

80

100

120

140

0 256 512 768 1024 1280 1536

Processors

Millio

n P

kt h

op

s/sec

Ideal/Linear

Target

Achieved

106 million

pkt hops/sec, 4 million

simulated nodes

Some of our recent milestones in parallel/distributed network simulations:• Record: Among the largest & fastest packet-level network simulations to date

• Shown: No. of packet transmissions simulated per second with pdns on Lemieux

• Benchmark: A “standard” benchmark (Campus network) scenario

Simulation Platform: Lemieux

Pittsburgh Supercomputing Centerwww.psc.edu/machines/tcs/lemieux.html

Reported inMASCOTS’03

Testing Network Defenses

• Interface actual securityapplications with virtualnetworks

– Transparent, plug-n-playinto large virtual “networkbattlefield”

– Test against large-scalevirtual attack scenarios

– Requires TCP/IPemulation…

Monitoring/

Detection/Analysis

Real

Large-scale Network & Malware

Virtual

Example - Honeypot Emulation

• Attack actual honeypot with simulated worms

• Test honeypot operation and effectiveness

Honeypot

PDNS

TCP/IP Emulation

Visualization of Multiple Campus Networks

10 campus

networks

connected

together

Portions of “Military Enterprise” Networks

dartmouth3886 ornl9177

campus538

*Network snapshots from netanim visualizer

Run

Run

Run

Fast radio channel simulation

Radio wave propagation through a gap in a concrete wall.

t = 3.27E-9 t = 5.80E-9 t = 1.09E-8

A comparison of empirical and wave propagation models

Transmitter

92.770.7Log-distance path loss,n = 4

76.259.7Log-distance path loss,n = 3

112.4948.7Free space path losswith knife edgediffraction

59.748.7Free space path loss

78.358.3Wave equation

RX 2 pathloss

RX 1 pathloss

Model

Receivers

RX 2

RX 1

TX

RX 1TX

RX 2

In Conclusion

• ORNL has a substantial research program in large scalesystem modeling, with an emphasis on communicationsdependent systems

• Simulation integration– Component based simulation

– DoD High Level Architecture

• High performance computing for large scale systems– Parallel discrete event simulation

– High performance simulation with component based frameworks

• Communication Network Applications– M&S for network security

– Network emulation

– Wireless channel modeling

– Visualization and Sonification

• Modeling frameworks for complex systems