Edward Smith BT

Contents� Models and how they are used

� Discrete Event Simulation and its uses

� The basic format of a model� Events and interrupts and how they drive models � Events and interrupts and how they drive models

� How statistics are gathered and interpreted.

� Examples showing: � TCP performance

� OSPF behaviour

� Queuing

� Radio LANs.

� Modelling a contact centre

Modelling Principles

� Understand what you are trying to model

� Keep the model simple

� Know what to include and what to exclude.� Know what to include and what to exclude.

� Focus on key features

� Document your model

What is it used for� IP V6 networks

� MPLS Traffic Engineering

� Modelling wireless networks (802.11, LTE, Sensor networks.)networks.)

� MANETS and VANETS.

� Security, including DDOS attacks

� General Protocol performance

� Teaching network mechanisms.

Basic Structure

Enter Executive

Function Block

C-Code called by any FSM component

Header Block

Macro definitions, often define state

change

Temporary VariablesDo not persist across process

invocations

State Variables

Persistent across multiple process

invocations.

Process Model – Finite State Machine

Finite State Machine

Enter ExecutiveContains code that is executed on

entering the state

Exit ExecutiveContains code that executed on

leaving the state.

Transition ExecutiveContains actions that are associated

with a specific transition

Termination Block

Destroying a dynamic process

invokes this code.

DiagnosticBlock

C or C++ code used to output diagnostic s.

� Events held in Event List

� Tracked and co-ordinated by the Simulation Kernel.

� Simulation time advances as events processed.

Simulation kernel processes event at the head of event

Events

� Simulation kernel processes event at the head of event list and passes control to target queue or processor.

� Begin simulation interrupt starts simulation time

� Simulations end through: end of simulation, process command or a fatal error occurs.

Adding Statistical Variation� Seed specified for each model� Plugged into a random number

generator� Generate the desired distribution.� One way of doing this is a

technique known as Inverse Transform Sampling

� Trivial example generated using

Random Exponential Function0.013156 0.98693

0.247602 0.7806710.229863 0.7946420.54244 0.581328

0.645261 0.5245260.59365 0.552308

0.337984 0.7132070.900944 0.406186

0.44942 0.637998� Trivial example generated using Excel

0.44942 0.6379980.381069 0.6831310.212416 0.808628

0

0,2

0,4

0,6

0,8

1

1,2P

r

o

b

a

b

i

l

i

t

y

Exponential Function

Exponential Function

0

0,2

0,4

0,6

0,8

1

1,2

0 0,5 1 1,5

Pro

ba

bil

ity

Random Number

Exponential Distribution

Exponential Function

Traffic and Statistics� Packet by Packet traffic � explicitly through either a

process module or network level applications

� Back ground traffic can also be generated on links or across nodes.

Import back ground from live networks using Netflow or � Import back ground from live networks using Netflow or Sniffer traffic.

� Statistics gathered at the end of a simulation run are the main output of the modelling exercise.

� The standard models offer a set of predefined statistics.

� At process level can define, register and collect bespoke statistics.

Dangers� Errors in simulations or improper data analysis.

� Different packages may produce different results.

� Validate simulation models against the real world.

Document model and assumptions. � Document model and assumptions.

� Statistical significance of the results collected.

� Collect results when steady state reached.

� Select only appropriate of traffic flows

� Sensitivity Analysis for parameters varied.

Andel, T.R. and Yasinsac, A. On the Credibility of Manet Simulations, IEEE Computer, July 2006,48-54

Protocol Model - OSPF

Ping C to J

Ping B to D

Ping C to J

Ping B to D

Protocol Model TCP

87

Slajd 16

87 You need to note that this uses an FTP profile802083140; 2015-07-29

Slow Start

Reno 0.5% Packet Drop

Slow Start 0.5% Packet Drop

Tahoe 0.5% Packet Drop

Simple Queue Model

M/M/1 and M/D/1 – Simulation v Analytical

solution

0,02

0,025

0,03D

ela

y (

mS

)

M/M/1

0

0,005

0,01

0,015

0% 5% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% 55,0% 60,0% 65,0% 70,0% 75,0% 80,0% 85,0% 90,0% 95,0%

De

lay

(m

S)

Utilisation %

M/M/1

M/D/1

M/M/1 Sumulation

M/D/1 Simulation

Self Similar Traffic – Analytical Result

200

250

300

Av

era

ge

Qu

eu

ing

De

lay

mS M/M/1

H=0.8

H=0.75

H=0.7

H=0.65

Utilisation %

0

50

100

150

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Av

era

ge

Qu

eu

ing

De

lay

mS

H=0.65

From: On the Prediction of Average Queuing Delay with Self Similar Traffic. Y.G. Kim and P.S. Min Globecom’03,IEEE Global Telecommunications Conference, 1-5 Dec 2003, IEEE pp 2987-91.

0,1

0,12

0,14

0,16

H=0.5

H=0.65

Simulation of Pareto Arrivals Process

0

0,02

0,04

0,06

0,08

0% 5% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% 55,0% 60,0% 65,0% 70,0% 75,0% 80,0% 85,0% 90,0% 95,0%

De

lay

(m

S)

Utilisation %

H=0.65

H=0.7

H=0.75

H=0.8

M/M/1 data points

Radio LAN Model

Pre-emptive Queue

Arrival

orig-id

orig_port

ack

svc time

svc_time_remain

svc_start

instructions

svc_start

current_subq

current_priority

last_update_time

server_busy 01

Set from sub-queue

Get packet priority

current time

Current time

0init

Current time

0

svc_complete 0

init routineIdleservice startpacket arrival – arrived packetpacket arrival - pre-empt packetservice complete

Work_left last – last_update_time

work_left_last

total_busy_time

processing _rate

time_in_processor

original_svc_time

processing_delay

pk_svc_time

svc_complete

pk_instructions

old_svc_time

time_processedCurrent_time – stamp of pre-empted packet

op_interrupt_schedule(current_time+pk_svc_time)

old_svc_time –time_processed

current time

Server_busy*current time – last_update_time

pk_instructions/processing_rate

current_time –svc_start

time_in_processor – original_svc_time

Work_left last –(Server_busy*current time – last_update_time)

Total_busy_time –(Server_busy*current time – last_update_time)

Add to

time_in_processor=svc_time–original_svc_time

/** state (arrival) enter executives **/FSM_STATE_ENTER_FORCED (2, "arrival", state2_enter_exec, "jsd_prio [arrival enter execs]")FSM_PROFILE_SECTION_IN ("jsd_prio [arrival enter execs]", state2_enter_exec){/** These executives are encountered when a packet arrives on an input **//** stream. The incoming packet is enqueued in priority order in a subqueue. **//** If packet already in service, it will not be preempted. Acquire the arriving packet.*/

pkptr = op_pk_get (op_intrpt_strm());if (pkptr == OPC_NIL)

jsd_prio_error ("Unable to get packet from input stream.");/** Determine the remaining service time of the incoming packet. **//** Determine the remaining service time of the incoming packet. **//* If the svc_time field of the packet is set, we can *//* read the remaining service time directly from it. */if (op_pk_nfd_get (pkptr, "svc_time",&pk_svc_time) ==

OPC_COMPCODE_FAILURE)jsd_prio_error ("Unable to get remaining service time from packet.");if (pk_svc_time > 0){

/* Set a success flag to be used later. */svc_time_determined = 1;

}/* Otherwise we need to determine the number of instructions *//* in the packet. */

Queueing Delay from this model

Contact Centre Model

The Media

Gateway

20

25

30

35

40

Q

u

e

u

i

Analytical

Full_Model _Exponential

Comparison of Analytical model

and Simulation

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5

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15

20

210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227

i

n

g

%

Number of Agents

8

10

12

A

v

e

r

a

g

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t

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m

(

s

e

Calculated

Full Model -240 calls exponentialModel - Contention experienced at media gateway

Impact on Time in queue

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6

210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227

m

e

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q

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s)

Number of Agents

Full Model -500 calls - exponentialModel – No Contention at media gateway

Route Callto ACD

Call ArrivalReturn to wait For next event

Media Gateway Node

Media Gateway

Initialisation

Clean up call count

WaitOn Event

Return to wait For next event

For next event

CallCompletion

Queuing hereIncreases call duration

Which increases occupation at media gateway

Impact of Media Gateway

CallSource

MediaGateway

Agent Queue

One call every 1.5seconds

One call every 1.5Seconds – Minus Calls dropped

Increasing blockingAt media gateway Equilibrium

Will be established aseffects of queuing andBlocking balance

The Model is Adaptable

Bibliography� Andel, T.R. and Yasinsac, A., On the Credibility of Manet Simulations, IEEE Computer,

July 2006, pp 48-54.� Svensson, T. and Popescu, A., Development of laboratory exercises based on Opnet

Modeller, Blenkinge Institute of Technology, June 2003, available from http://staff.ustc.edu.cn/~bhua/experiments/Lab_Exercices_Modeler.pdf

� Smith, E.A., The Goals and Benefits of Network Modelling in a Commercial Environment, Proceedings of 51st FITCE Congress, Poznan, September 6th and 7th 2012, paper #1569593947.Environment, Proceedings of 51 FITCE Congress, Poznan, September 6 and 7 2012, paper #1569593947.

Also published in: Journal of Telecommunications and Information Technology, 2013, No 1, pp 25 – 31� Smith, E.A., A Brief Examination of Network Modelling, Journal of the Institute Of

Telecommunications Professionals, 2013, 7(1), pp 37-42. � Smith, E.A., Statistical Modelling of a Contact Centre, Journal of The Institute Of

Telecommunications Professionals, 2015, 9(2), pp 23-30. � Smith, E.A., Principles of using formal modelling techniques, unpublished, copy

available from author on request. � Smith, E.A., Modelling queuing behaviour using Opnet modeller, unpublished, copy

available from author on request.