How to Calculate Network Event Correlation in NS2
To calculate the Network Event Correlation in NS2, we have to assess and link the various events that occur during the simulation to know their relationships between them. It helps to identify the network issues, detecting patterns and enhancing network performance by defining how one event (like packet loss, congestion or delay) is relevant to another (such as bandwidth utilization, latency or node failure). In NS2, events include packet transmission, reception, loss and delay are logged in the trace file and these events can be correlated by evaluating their time stamps, source/destination nodes and event types. Here in the below, we offered the computational steps to measure it in ns2:
Steps to Calculate Network Event Correlation in NS2
- Set Up the NS2 Simulation for Event Logging
Start by developing the ns2 simulation to log all vital events in a trace file. We can detect the correlations amongst them like packet drops, delays and bandwidth consumption by analysing the trace file.
Example NS2 Script for Event Logging
# Create NS2 simulator instance
set ns [new Simulator]
# Open trace file for logging network events
set tracefile [open event_correlation_trace.tr w]
$ns trace-all $tracefile
# Define network nodes
set node1 [$ns node] ;# Client node
set node2 [$ns node] ;# Gateway node
set node3 [$ns node] ;# Server node
# Create duplex links between nodes
$ns duplex-link $node1 $node2 1Mb 10ms DropTail
$ns duplex-link $node2 $node3 1Mb 10ms DropTail
# Set up UDP traffic from node1 to node3 via node2
set udp0 [new Agent/UDP]
$ns attach-agent $node1 $udp0
set null0 [new Agent/Null]
$ns attach-agent $node3 $null0
$ns connect $udp0 $null0
set cbr0 [new Application/Traffic/CBR]
$cbr0 set packetSize_ 500
$cbr0 set interval_ 0.01
$cbr0 attach-agent $udp0
# Start and stop traffic
$ns at 0.5 “$cbr0 start”
$ns at 4.5 “$cbr0 stop”
# End simulation
$ns at 5.0 “finish”
proc finish {} {
global ns tracefile
$ns flush-trace
close $tracefile
exit 0
}
# Run the simulation
$ns run
Explanation:
- Trace File: The trace file event_correlation_trace.tr logs all network events as well as transmissions, receptions, and drops, which will be used for event correlation analysis.
- Network Setup: UDP traffic flows amongst node1 and node3 via node2. Events like packet transmission, reception, and drops will be logged.
- Analyze the Trace File for Event Correlation
The trace file has the records of multiple events with details including time, event type (transmission, reception, drop), source and destination nodes, and packet size. You can correlate events in terms of their timestamps, types, and the nodes involved.
Example Trace File Entries:
+ 0.500000 0 1 udp 500 ——- 1 0.0 0.0 1.0
– 0.510000 0 1 udp 500 ——- 1 0.0 0.0 1.0
r 0.515000 1 2 udp 500 ——- 1 0.0 0.0 1.0
d 0.520000 1 2 udp 500 ——- 1 0.0 0.0 1.0
- +: Packet enqueued at source node.
- -: Packet dequeued and transmitted.
- r: Packet received at destination node.
- d: Packet dropped.
- Define Correlation Metrics
Common event correlations include:
- Packet loss vs. bandwidth usage: Define how packet loss correlates with bandwidth utilization.
- Packet delay vs. latency: Correlate the delay in packet dispatching with end-to-end latency.
- Packet drops vs. congestion: Assess whether packet drops maximize with higher traffic load, representing congestion.
- Calculate Correlation Metrics Using AWK
Packet Loss vs. Bandwidth Usage Correlation
This correlation helps state how packet loss varies as bandwidth utilization maximizes. First, measure the total packet loss and total bandwidth usage, then evaluate how they are related.
Here’s an AWK script to calculate packet loss and bandwidth usage:
awk ‘
{
if ($1 == “d”) { # Count dropped packets
dropped_packets++;
}
if ($1 == “+” && $3 == “node1”) { # Calculate bandwidth usage from node1
total_bytes_sent += $6;
}
}
END {
print “Total Dropped Packets:”, dropped_packets;
print “Total Bandwidth Usage (bytes):”, total_bytes_sent;
}’ event_correlation_trace.tr
This script estimates the count of dropped packets and the total bytes delivered, which can be used to evaluate the relationship amongst packet loss and bandwidth usage.
Packet Delay vs. Latency Correlation
This correlation encompasses computing the time delay among when a packet is sent and when it is obtained, and correlating that with entire network latency.
Here’s an AWK script to calculate packet delay and latency:
awk ‘
{
if ($1 == “+”) {
send_time[$7] = $2; # Record the time the packet is sent
}
if ($1 == “r” && $3 == “node3”) { # Packet received at node3
if (send_time[$7] != “”) {
delay = $2 – send_time[$7]; # Calculate the delay
total_delay += delay;
count++;
}
}
}
END {
average_delay = total_delay / count;
print “Average Packet Delay:”, average_delay, “seconds”;
}’ event_correlation_trace.tr
This script estimates the average packet delay by relating the timestamps of sent and received packets, which can be correlated with network latency.
Packet Drops vs. Traffic Load Correlation
This correlation measures how packet drops are impacted by extreme traffic load. You can assess how traffic volume (measured by bytes sent) relates to packet drops.
Here’s an AWK script to correlate packet drops with traffic load:
awk ‘
{
if ($1 == “d”) { # Count dropped packets
dropped_packets++;
}
if ($1 == “+” && $3 == “node1”) { # Calculate total traffic load from node1
total_traffic += $6;
}
}
END {
print “Total Dropped Packets:”, dropped_packets;
print “Total Traffic Load (bytes):”, total_traffic;
}’ event_correlation_trace.tr
This script calculates both dropped packets and traffic load, which can be used to monitor if packet drops rise with higher traffic.
- Perform Event Correlation Analysis
Once you’ve calculated the relevant metrics, you can perform correlation analysis by comparing the values. For instance, you can compute the correlation coefficient amongst packet loss and bandwidth utilization to observe if they are positively or negatively correlated.
Python Example for Calculating Correlation
Calculate the correlation amongst two metrics (like packet loss and bandwidth usage) by using Python. Here’s a basic sample using Python’s numpy library:
import numpy as np
# Example data for packet loss and bandwidth usage
packet_loss = [10, 15, 20, 25, 30] # Dropped packets
bandwidth_usage = [500000, 700000, 800000, 950000, 1100000] # Bytes
# Calculate correlation coefficient
correlation = np.corrcoef(packet_loss, bandwidth_usage)[0, 1]
print(“Correlation between packet loss and bandwidth usage:”, correlation)
This Python code calculates the correlation coefficient between packet loss and bandwidth usage, giving you insight into how these metrics are related.
- Interpret the Correlation Results
Once you have calculated the correlation values, interpret the outcomes:
- Positive correlation: A high positive correlation represents that as one metric rises (such as bandwidth usage), the other also rises (like packet loss).
- Negative correlation: A high negative correlation means that as one metric increase, the other minimize (e.g., higher bandwidth usage causing lower packet delays).
- No correlation: A correlation coefficient near zero signifies that the two metrics are unrelated.
Summary
To measure network event correlation in NS2:
- Set up the simulation: Log all events in a trace file to track packet transmissions, receptions, and drops.
- Identify correlation metrics: Pick metrics like packet loss vs. bandwidth usage, packet delay vs. latency, or packet drops vs. traffic load.
- Analyze the trace file: Use AWK scripts to estimate the metrics for each event type.
- Perform correlation analysis: Quantify the correlation among metrics using Python or other statistical tools.
- Interpret the results: Use correlation coefficients to know the relationships between various network events.
Through this manual, we have utterly learned the concepts to help you with the measurement of network event correlation by offering the expounded information including examples with snippet codes. If needed, we will provide any additional details of event correlation or how to detect the relationships.
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