Skip to main content


Showing posts from March, 2017

PyTorch Code for Simple Neural Networks for MNIST Dataset

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 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

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

Multicasting in NS2

Mutlicast routing in ns2 can be easily set using any of the following syntax Method 1:         set ns [new Simulator -multicast on] Method 2:         set ns [new Simulator]         $ns multicast when multicast mode is enabled, the nodes behave differently with the additional classifiers are created. A distribution tree kind of structure is created in the simulation when dealing with multicast routing.  NS supports three types of multicast routing: Centralized Multicast Routing(CM), Dense Mode (DM), Shared Tree Mode (ST) and Bi directional Shared Tree Support (BST). Here is the method to specify the multicast routing in ns. set cmc [$ns mrtproto CtrMcast]    # specify centralized multicast for all nodes; # cmc is the handle for multicast protocol object; $ns mrtproto DM                   # specify dense mode multicast for all nodes; $ns mrtproto ST                  # specify shared tree mode to run on all nodes; The agents in the protocol

Error "bailing in Tcl::eval"

In ns2, if you ever encounter a error like "bailing in Tcl::eval", then it could be due the size of the buffer that Tcl:: eval may offer By default the buffer size is 1024 and it can be increased to 2048 (for example) Usually this error will be encountered when you increase the number of nodes in the simulation (for example more than 100 nodes might give you this error) bailing in Tcl::eval How to overcome this error? Here is a small workaround Open the command prompt and go the ns-allinone-2.35/tclcl-1.xx/ folder and open the file $] cd ns-allinone-2.35/tclcl-1.20/ (in my case) in the following function (Line number: 202, change 1024 to 2048 ) as shown below void Tcl::eval() { char* p = bp_; bp_ = p + strlen(p) + 1; /*XXX*/ if (bp_ >= &buffer_[ 2048 ]) { fprintf(stderr, "bailing in Tcl::eval\n"); assert(0); exit(1); } eval(p); bp_ = p; } Once the changes are made, reinstall ns2 itself $] cd ns-allino