Network simulations and tools - users.jyu.fiusers.jyu.fi/~timoh/kurssit/jatkoksem/simulations.pdf · Network simulations and tools ... NS3: Main objects, ... stack Application class

Network simulations and tools

Dmitry Petrov

dmitry.petrov@ magister.fi or jyu.fi

How may networks be studied?

Measurements from real devices / networks

Measurements from real devices

Protocol analyzers, i.e. “sniffers”

Operator conducted drive tests with specialized equipment

Networks / devices may not exist for state-of-the art wireless technologies

Test networks

Access to all equipment / software

Open-source software

Test networks may not be large enough

Computer simulations

Faster to achieve results

Easier to analyze with different assumptions

How reliable are the results?

Mathematical analysis

Pen’n’paper, Matlab, Mathematics, etc.

2 © 2012 Magister Solutions Ltd

Magister Solutions – From science into business


Mobile Applications

Active

In-house mobile software

development

R&D Training Analytics

New Active Active

Educational, academic and expert networks

Universities EDU cluster

VTT Corporate

development

“Essentially, all models are wrong, but some are useful”

[George E. P. Box and Norman R. Draper. Empirical Model-Building and Response Surfaces. Wiley, 1987]


When to use simulations?

When the analytical model/solution is not possible or feasible

Many times, simulation results are used to verify analytical solutions in order to make sure that the system is modeled correctly using analytical approaches.

Dynamic systems, which involve randomness and change of state with time

Complex dynamic systems, which are so complex that when analyzed theoretically will require too many simplifications. In such cases, it is not possible to study the system and analyze it analytically.


What are simulations used for?

Generally

Test improvements and their gains

To identify bottlenecks in a process

To prevent under of over-utilization of resources

Provide a safe and relatively cheaper (in term of both cost and time) test bed to evaluate the side effects

Optimize the performance of the system


Simulation scenarios

With wrap-around radio conditions of the actual simulation area are replicated around the actual simulation area where UEs are located

Non-regular layout


R&D examples


Spectrum 1 Spectrum 2

5 MHz

DC capable UE

Multipoint Tx

0 5 10 15 20 25 30 350

5

10

15

20

25

30

35

40

45

50

Users per cell

Mean S

ofter

HO

User

BR

Gain

(%

)

PA3 Channel (Softer Handover Users)

Tokyo example

Goal:

Performance benchamarking between 3G HSDPA and next generation LTE system

Challenges:

It is hard to collect needed statistics from commercial networks

It is not affordable to build large enough test networks

In relation to LTE, there was only limited commercial products available


References – Case Tokyo

Simulation based approach was selected

Like many vendors, operators as well as the scientific communities do for studying the wireless cellular network performance

Digital network planning data over Tokyo map was used in the simulator

Realistic conditions through non-regular network layout and propagation


Available simulators

Most suitable:

Network Simulator-2 (NS-2) http://isi.edu/nsnam/ns/

Network Simulator-3 (NS-3) http://www.nsnam.org/

OMNeT ++ http://www.omnetpp.org/

openWNS https://launchpad.net/openwns

Other open

JiST http://jist.ece.cornell.edu/

GoMoSim http://pcl.cs.ucla.edu/projects/glomosim/

Proprietary

OPNeT http://www.opnet.com/

QualNet http://www.scalable-networks.com/products/qualnet/

NetSim http://tetcos.com/software.html

Company owned


http://isi.edu/nsnam/ns/

http://isi.edu/nsnam/ns/

http://www.nsnam.org/

http://www.nsnam.org/

http://www.omnetpp.org/

http://www.omnetpp.org/

https://launchpad.net/openwns

http://jist.ece.cornell.edu/

http://pcl.cs.ucla.edu/projects/glomosim/

http://www.opnet.com/

http://www.opnet.com/

http://www.scalable-networks.com/products/qualnet/




http://tetcos.com/software.html

http://tetcos.com/software.html

Simulator selection criteria Parameter NS-2 NS-3 OMNeT++ openWNS

License GNU GPLv2 GNU GPLv2 Free for academy

and education LGPL

Wireless technologies

+ + + + -

Support/development

+ - + + + -

Real time integration

+ -

+ + -

Platform C++ and OTcl C++ and Python C++ and NED Python with

C++ libraries

Performance - + -


NS 3 Organization

tests

helper

routing Internet stack

devices applications

node mobility

common simulator

core

Smart pointers, Dynamic type systems, Attributes, Callbacks,

Tracing, Logging, Random Variables

Events, Schedulers, Time arithmetic

Packets, Packet tags, Packet

headers, Pcap/Ascii file writing

Mobility models (static, random

walk, etc.)

Node class, NetDevice, Address types (Ipv4, MAC, …), Queues,

Socket, Packet sockets


NS3: Main objects, attention to realism

Node class

Protocol stack

Application class

Channel class

Net device class

Topology helpers

Packets

Sockets-like API

• Model nodes like real computer • Support key interfaces such as sockets API


Discrete-event simulation

The idea is to jump from one event to another

Events are recoded in a future event list (FEL)

Each event notice is composed at least to data: time, type

Event routine or handler to process each event

Each event may change the system state and generate new event notices


Simulator types

Different ways of dividing simulators

Running principle: discrete event, continuous, …

Level of detail: static, quasi-static, dynamic, …

Modeling purpose: wireless radio access, wired, …

Confidentiality: proprietary, open


Simulation dynamics

Moving randomly

Yes

Fully modelled RRM

Dynamic simulations

Varied every timeslot

(slot, TTI, etc.)

Mobility Study, RRM

Study, etc.

Slow

Static simulations “Snapshot”

Static

No

Simplified and limited

algorithms, e.g no outer

power control

Fixed

Algorithms

Path Loss

Time Domain

Mobility Static

Extensively modelled

RRM

Fixed

Yes

Quasi-Static Simulations

RRM study excluding

mobility, etc.

Middle

Interference study between

systems, etc. USE Cases

Fast Computational

Complexity

System simulators


Link and system level simulators

(Mobile) network simulators can be roughly categorized in link level and system level simulators

Link level simulators model in high detail the radio interface between a MT and BS. Link level simulators operate on chip or symbol level

System level simulators model a full network usually including multiple BSs and number of mobile stations. Slot-level modeling is sufficient for system simulation (1 slot = 2560 chips = 0.66667 ms)

Link level simulator output works as input for system level simulators using mapping tables or curves


Example of link level curves


Propagation, slow and fast fading


Mobility models

Random walk Random waypoint


Traffic models

Defined by standardization

organizations

Infinite buffer

FTP

Constant Bit Rate (CBR)

HTTP models (bursty)

AMR codecs for VoIP


Dpc

Nd

packet callpacket call

embedded objects

(Reading Time)

main object

Packet calls

Dpc

Packets of file 1 Packets of file 2 Packets of file 3

Metrics and performance indicators

Cumulative distribution function (CDF)

Line plots

Bar plots

Map plots

Traces


Generic architecture

Simulations

...other

Parameters

Results

Run/debug/visualise

encoder decoder

Player (NetAnim, PyViz,…)

Scenarios


Post-processing tool

Demos

802.11s Mesh network

Directed antennas

Energy models

Energy + air-time routing

LTE network

Thank you for attention!

[email protected]

mailto:[email protected]

Documents

Network simulations and tools - users.jyu.fiusers.jyu.fi/~timoh/kurssit/jatkoksem/simulations.pdf · Network simulations and tools ... NS3: Main objects, ... stack Application class