How to Implement Network Analytics in NS2

To implement the Network Analytics within the simulation platform NS2 that encompasses gathering, evaluating, and also visualizing the network performance metrics like throughput, latency, packet loss, and congestion. It can attain by collecting the data from the trace files are made while a replicating and processing these data to get the insights into how the network is executing. This network analytics is helpful for diagnosing issues, enhancing the network performance, and comprehending the traffic patterns. Obtain some of the finest project ideas from ns2project.com tailored to your research area, as we provide timely implementation support. We provide more vital process to execute the network analytics within NS2:

Key Steps to Implement Network Analytics in NS2:

Simulate Network Traffic: Make a network topology and generate various kinds of the traffic.
Collect Network Metrics: It can use the trace files to log the network events like packet transmissions, receptions, and drops.
Analyze Metrics: Process the trace file to extract helpful metrics like throughput, delay, packet loss, and congestion.
Visualize Results: Plot the data or generate reports for a well comprehending of network performance.

Common Metrics in Network Analytics:

Throughput: The rate at which the data is effectively delivered across the network.
Packet Loss: The number of packets are dropped because of the network congestion or errors.
Latency: The latency experienced by packets as they traverse the network.
Jitter: Differences in the packet delay.
Congestion: Indications of network congestion, like packet drops and queue overflows.

Example: Simulating Network Analytics in NS2

In this instance, we will:

Make a basic network topology.
Generate various kinds of traffic.
We use the trace file to gather the data on throughput, delay, and packet loss.
Estimate the gathered metrics.

Example TCL Script for Network Simulation:

# Create a new simulator instance

set ns [new Simulator]

# Define output trace file for logging events

set tracefile [open network_analytics.tr w]

$ns trace-all $tracefile

# Define animation file for NAM (optional)

set namfile [open network_analytics.nam w]

$ns namtrace-all $namfile

# Create network nodes: Clients and Server

set client1 [$ns node] # Client 1

set client2 [$ns node] # Client 2

set router [$ns node] # Router

set server [$ns node] # Server

# Create duplex links between clients, the router, and the server

$ns duplex-link $client1 $router 10Mb 10ms DropTail

$ns duplex-link $client2 $router 10Mb 10ms DropTail

$ns duplex-link $router $server 50Mb 10ms DropTail

# —————- Traffic Simulation —————-

# Define traffic for Client 1 (TCP – File Transfer)

set tcp_client1 [new Agent/TCP]

$ns attach-agent $client1 $tcp_client1

set tcp_sink [new Agent/TCPSink]

$ns attach-agent $server $tcp_sink

$ns connect $tcp_client1 $tcp_sink

set ftp_client1 [new Application/FTP]

$ftp_client1 attach-agent $tcp_client1

# Define traffic for Client 2 (UDP – Video Streaming)

set udp_client2 [new Agent/UDP]

$ns attach-agent $client2 $udp_client2

set udp_sink [new Agent/Null]

$ns attach-agent $server $udp_sink

$ns connect $udp_client2 $udp_sink

set cbr_client2 [new Application/Traffic/CBR]

$cbr_client2 attach-agent $udp_client2

$cbr_client2 set packetSize_ 1000

$cbr_client2 set rate_ 2Mb

$cbr_client2 set interval_ 0.01

# —————- Simulation Control —————-

# Schedule the traffic flows

$ns at 0.5 “$ftp_client1 start”

$ns at 0.5 “$cbr_client2 start”

# Schedule the stop time for traffic

$ns at 4.0 “$ftp_client1 stop”

$ns at 4.0 “$cbr_client2 stop”

# End the simulation at 5.0 seconds

$ns at 5.0 “finish”

# Define finish procedure to close trace files and execute NAM for visualization

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam network_analytics.nam &

exit 0

}

# Run the simulation

$ns run

Simulate Network Traffic

In this script:

Client 1 generates TCP traffic (representing a file transfer).
Client 2 makes a UDP traffic (signifying video streaming).
The router sends the traffic to the server, and also we use the DropTail queuing.

The simulation records all events into a trace file (network_analytics.tr) that will later be evaluated.

Collecting Metrics from the Trace File

The trace file includes data like:

Time: While the event happened.

Event type: Whether the packet was sent (+), received (r), or dropped (d).
Packet details: Source, end, packet size, etc.

Analyzing Metrics Using awk

We can estimate the trace file using the tools such as awk to extract helpful parameters like throughput, delay, and packet loss. Given below are some general instances.

a) Throughput Calculation:

Throughput is the rate of effective data delivery. To evaluate the throughput for TCP traffic (from Client 1 to the Server):

awk ‘$1 == “r” && $4 == “tcp” { total_bytes += $5 } END { print “Throughput: “, total_bytes/5, “bytes/sec” }’ network_analytics.tr

Explanation:

r: Specifies the packet is received.
tcp: We filter only TCP packets.
total_bytes: The sum of bytes received throughout the simulation.
We split by 5 to get throughput in bytes per second (assuming a 5-second simulation).

b) Packet Loss Calculation:

Packet loss can determine by counting the amount of dropped packets:

awk ‘$1 == “d” { total_dropped++ } END { print “Packet Loss: “, total_dropped }’ network_analytics.tr

Explanation:

d: Indicates the packet was dropped.
We count the total amount of dropped packets.

c) Latency Calculation:

Latency is the delay a packet experiences from the moment. It is forwarded to while it is received. To estimate the average latency for TCP traffic:

awk ‘

/^s/ && $4 == “tcp” { sent[$6] = $2 }

/^r/ && $4 == “tcp” { if ($6 in sent) { total_delay += ($2 – sent[$6]); total_packets++ } }

END { print “Average Latency: “, total_delay/total_packets, “seconds” }’ network_analytics.tr

Explanation:

We can store the send time of each packet using its one and only ID ($6).
Once the packet is received (r), we assess the variations among the send and receive times to get the delay.
We compute the average latency by splitting the total delay by the number of packets.

Visualizing Results

We can envision the outcomes using a plotting tool such as Gnuplot, Matplotlib (Python), or other visualization tools. For instance, if we need to plot the throughput across time:

awk ‘$1 == “r” && $4 == “tcp” { print $2, $5 }’ network_analytics.tr > throughput.dat

The above command extracts the time ($2) and the size of each received packet ($5) into a file throughput.dat. We can then plot these data using the preferred plotting tool.

Advanced Network Analytics Features

We can improve the analytics by:

Measuring Jitter: Estimate the jitter as the difference in packet delay for real-time traffic (e.g., video streaming).

Analyzing Congestion: Find the duration of congestion by observing the packet drops and queue use at the router.
Load Balancing Analysis: If several paths are obtainable, estimate the traffic distribution and load balancing effectiveness.
QoS (Quality of Service) Analysis: Assess how various kinds of traffic (e.g., TCP vs. UDP) behave under changing the network conditions.

Example: Analyzing Jitter for UDP Traffic

Jitter is the difference in delay among the consecutive packets. To estimate jitter for UDP traffic:

awk ‘

/^r/ && $4 == “udp” { if (last_time != “”) { jitter += ($2 – last_time) > 0 ? $2 – last_time : last_time – $2; count++ } last_time = $2 }

END { print “Average Jitter: “, jitter/count, “seconds” }’ network_analytics.tr

To conclude, we detailed the steps and gave relevant example of carrying out and evaluating the Network Analytics in NS2, following the above method. Further details will be made available if required.