Queuing Models in NS2

M/M/1 is a system with poisson arrival time, servicing exponentially and a queue of unlimited capacity and type of FIFO Queue. This is the simplest queuing system.  NS2 supports various distributions like pareto, exponential, constant, unifrom, etc to handle the network dynamics and metrics. So it is very easy to test the given network link to monitor a given queue using any of these queuing models. The listing 3 and 4 are monitoring the link when DropTail queue is used with a capacity of finite and infinite. Listing 13.3 uses infinite capacity and Listing 13.4 uses Finite capacity The output screen shot is shown below the scripts for further understanding
Listing 3 – M/M/1 Queuing Model #new Simulator creation set ns [new Simulator] #trace file creation for capturing the UDP data set tf [open out.tr w] $ns trace-all $tf
#setting the exponential distribution param set lambda 30.0 set mu     33.0
#creation of nodes set n1 [$ns node] set n2 [$ns node] #The queue limit is 1Lakh as the capacity is infin…

Random Number Generation using ns2

Random variables is an important concept in networks as the modeling of network traffic and other packet arrival times are mostly random models. Hence there is a necessity of modeling such metrics in ns2. NS2 supports various random models using different seed generations.

Seeds are numbers that are helpful in generating the random numbers. The seed number 0 indicates that the random number order changes every time the simulation is running. But other than 0, the order in which the random number generated are same.

The following listing 1 shows the random number generation for various distributions. This listing will just tell you how to create random number generation for various distributions. For seeing the output, refer to listing 2

Listing 1 – Random Number Generation Sample
#create the random number generation using RNG
set rand1 [new RNG]
set rand2 [new RNG]
set rand3 [new RNG]
#$repli is the value set already and it can be either 1 or <1 or >1
for {set i 1} {$i < $repli} {incr i} {
$rand1 next-substream;
$rand2 next-substream;
$rand3 next-substream;

Here are the distributions
Pareto Distribution
set r1 [new RandomVariable/Pareto]
$r1 use-rng $rand1
$r1 set avg_ 10.0
$r1 set shape_ 1.2

Constant – Specifies the constant number
set r2 [new RandomVariable/Constant]
$r2 use-rng $rand2
$r2 set val_ 5.0

Uniform distribution – Specifies the min and max number
set r3 [new RandomVariable/Uniform]
$r3 use-rng $rand3
$r3 set min_ 0.0
$r3 set max_ 10.0

Exponential distribution.- Specified the average value:
set r4 [new RandomVariable/Exponential]
$r4 use-rng $rand1
$r4 set avg_ 5

Hyperexponential distribution
set r5 [new RandomVariable/HyperExponential]
$r5 use-rng $rand2
$r5 set avg_ 1.0
$r5 set cov_ 4.0

The following listing 2 creates the random number generation for two different distributions uniform and exponential. The replication number is supplied through the command line option. If no replication number is supplied, it will be taken as 0 and it a value less than 1 is supplied then, it will be running with value 1. So the following example uses the replication number as 0, 1 or any higher number > 1 and it uses a seed value of 9999
The syntax for setting the seed value is

$defaultRNG seed N

Where N is a interger which may take 0 or 1 or any positive number

Listing 2 – Random number generation – Case 1
#filename : randtest1.tcl
# Usage: ns randtest.tcl [replication number]

if {$argc > 1} {
    puts "Usage: ns randtest1.tcl \[replication number\]"
set run 1
if {$argc == 1} {
    set run [lindex $argv 0]
if {$run < 1} {
    set run 1

# seed the default RNG
global defaultRNG
$defaultRNG seed 9999

# create the RNGs and set them to the correct substream
set arrivaldist [new RNG]
set size [new RNG]
for {set j 1} {$j < $run} {incr j} {
    $arrivaldist next-substream
    $size next-substream

# arrival_ is a exponential random variable describing the time between
# consecutive packet arrivals
set arrival_ [new RandomVariable/Exponential]
$arrival_ set avg_ 5
$arrival_ use-rng $arrivaldist

# size_ is a uniform random variable describing packet sizes
set size_ [new RandomVariable/Uniform]
$size_ set min_ 100
$size_ set max_ 5000
$size_ use-rng $size

# print the first 5 arrival times and sizes
for {set j 0} {$j < 5} {incr j} {
    puts [format "%-8.3f  %-4d" [$arrival_ value] \
            [expr round([$size_ value])]]
Here is the sample output for above file
Prompt] ~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 0
6.358     4783
5.828     1732
1.469     2188
0.732     3076
4.002     626
Prompt]~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 1
6.358     4783
5.828     1732
1.469     2188
0.732     3076
4.002     626
Prompt]~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 2
2.091     153
12.085    4732
3.588     2329
1.201     230
5.161     2980
Prompt]~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 3
2.515     1119
3.154     3118
9.673     1201
13.346    2515
7.052     2115
Prompt]~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 100
8.940     2149
0.888     4870
0.998     4860
1.801     1205
18.224    2534
Prompt]~/ns-allinone-2.35/ns-2.35/tcl/ex $ ns randtest1.tcl 0.5
6.358     4783
5.828     1732
1.469     2188
0.732     3076
4.002     626

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