How to Calculate Network Load Balancing in Ns2
To calculate the network load balancing within NS2 (Network Simulator 2), which encompasses determining how evenly traffic is delivered over the network’s links or nodes. A well-balanced network will be delivered the traffic more evenly and avoiding bottlenecks at particular nodes or links, whereas a poorly balanced network can observe some nodes or links overloaded although others are underutilized.
To assess and estimate the load balancing in NS2, we can focus on various metrics like:
- Link load distribution: How traffic is distributed over the network links.
- Node load distribution: The amount of traffic (packets processed) by each node.
- Resource utilization: CPU or bandwidth usage at each node or link.
Steps to Calculate Network Load Balancing in NS2
- Define Load Balancing Metrics
We can use the metrics to estimate the load balancing based on the type of network and particular goals. Usual metrics include:
- Packet throughput at each node or link.
- Queue length at each node or link.
- Link utilization: The percentage of bandwidth used on each link.
- Packet drop rate: These nodes or links with higher loads may drop more packets.
- Traffic flow: The number of packets are transmitted or received by each node.
- Set up the Simulation in NS2
We will be required to make a simulation with several nodes and links, in addition to define the traffic flows among various nodes.
Given below is a basic instance of a network topology in NS2:
# Create NS2 simulator instance
set ns [new Simulator]
# Open trace file for output
set tracefile [open out.tr w]
$ns trace-all $tracefile
# Define nodes
set n0 [$ns node]
set n1 [$ns node]
set n2 [$ns node]
set n3 [$ns node]
set n4 [$ns node]
# Create links between nodes (set bandwidth and delay)
$ns duplex-link $n0 $n1 1Mb 10ms DropTail
$ns duplex-link $n1 $n2 1Mb 10ms DropTail
$ns duplex-link $n2 $n3 1Mb 10ms DropTail
$ns duplex-link $n3 $n4 1Mb 10ms DropTail
$ns duplex-link $n0 $n3 1Mb 10ms DropTail ;# Extra link to see load balancing
# Set up traffic (UDP traffic)
set udp0 [new Agent/UDP]
$ns attach-agent $n0 $udp0
set null0 [new Agent/Null]
$ns attach-agent $n4 $null0
$ns connect $udp0 $null0
set cbr0 [new Application/Traffic/CBR]
$cbr0 set packetSize_ 500
$cbr0 set interval_ 0.005
$cbr0 attach-agent $udp0
# Schedule events
$ns at 0.5 “$cbr0 start”
$ns at 4.5 “$cbr0 stop”
$ns at 5.0 “finish”
proc finish {} {
global ns tracefile
$ns flush-trace
close $tracefile
exit 0
}
# Run simulation
$ns run
- Enable Queue Monitoring
For load balancing, we will frequently want to observe the queue lengths and use on various links. We can be used the queue-monitor command to observe how traffic is distributed over the links.
Example for enabling queue monitoring on links:
# Enable queue monitoring for the link n0-n1 and n1-n2
$ns queue-monitor $n0 $n1 [open “queue-n0-n1.tr” w]
$ns queue-monitor $n1 $n2 [open “queue-n1-n2.tr” w]
This makes a trace files (queue-n0-n1.tr, queue-n1-n2.tr, etc.), which log queue events, like enqueue and dequeue operations, providing insight into load balancing on those links.
- Collect Data From Trace Files
The trace file (out.tr) logs key events like packet enqueuing (+), dequeuing (-), and dropping (d). These events can be examined to compute the load distribution over various links or nodes.
Example trace file entries:
+ 0.500000 0 1 tcp 1000 ——- 1 0.0 0.0 1.0
– 0.501234 0 1 tcp 1000 ——- 1 0.0 0.0 1.0
d 0.501234 0 1 tcp 1000 ——- 1 0.0 0.0 1.0
We can use these events to compute:
- Throughput on each link or node (by counting dequeued packets).
- Queue length on each node (by counting enqueued and dequeued packets over time).
- Packet drop rates on each link or node.
- Calculate Load Balancing Metrics Using AWK
We can write an AWK script to extort load-related data from the trace file.
Example 1: Calculating Throughput Per Link
To assess the number of packets are sent on each link, we can be used an AWK script:
awk ‘$1 == “-” && $4 == “tcp” { link_count[$3″->”$4]++ }
END { for (link in link_count) print “Link:”, link, “Throughput:”, link_count[link] }’ out.tr
This script:
- Counts how many packets are dequeued (- event) from every single link.
- Outcomes the throughput (in terms of number of packets) for each link.
Example 2: Calculating Link Utilization
Link utilization can estimate as the ratio of the real throughput to the link capacity. We can be expanded the AWK script to contain time intervals and link bandwidth:
awk ‘BEGIN { link_capacity = 1e6; sim_time = 5.0 }
$1 == “-” && $4 == “tcp” { link_count[$3″->”$4]++ }
END {
for (link in link_count) {
throughput_bps = (link_count[link] * 8 * 500) / sim_time; # Assuming 500 bytes per packet
utilization = throughput_bps / link_capacity * 100;
print “Link:”, link, “Utilization:”, utilization “%”;
}
}’ out.tr
Example 3: Queue Length Calculation
We can be computed the queue lengths for each link by counting enqueue (+) and dequeue (-) events:
awk ‘
{
if ($1 == “+”) {
enqueue[$3]++;
}
if ($1 == “-“) {
dequeue[$3]++;
}
}
END {
for (link in enqueue) {
print “Link”, link, “Queue Length:”, enqueue[link] – dequeue[link];
}
}’ out.tr
It will be delivered the queue length on each link at various points in time and permitting to evaluate how successfully traffic is distributed.
- Post-Processing Load Balancing Data
When we have gathered throughput, queue length, or link utilization data from the trace files then we can assess how evenly traffic is distributed over the network.
We can estimate:
- Variance in link utilization: A high variance intimates that some links are heavily loaded but others are underutilized, showing poor load balancing.
- Maximum and minimum link utilization: Compare the most loaded link with the minimum loaded link.
- Average queue length: A large variance in queue lengths between the nodes intimates load imbalance.
- Visualizing Load Balancing
We can visualize the load balancing metrics using plotting tools such as Python (matplotlib) or Excel.
Example Python Plot for Link Utilization:
import matplotlib.pyplot as plt
# Example data for link utilization
links = [‘n0->n1’, ‘n1->n2’, ‘n2->n3’, ‘n3->n4’]
utilization = [50, 80, 20, 60] # Example utilization percentages
plt.bar(links, utilization)
plt.title(‘Link Utilization’)
plt.xlabel(‘Links’)
plt.ylabel(‘Utilization (%)’)
plt.show()
This plot supports to envision how balanced the traffic is over the various links.
- Analysing Load Balancing Results
- Good load balancing: Utilization over the links and nodes are roughly equal, and no single link or node is heavily loaded.
- Poor load balancing: Some links or nodes have high utilization although others have very less utilization. It could lead to congestion in particular parts of the network, but other parts are remain underutilized.
Hence, we followed the above methods to compute and analyse the Network Load Balancing that focus on various metrics such as link load distribution, node load distribution, and resource utilization in the virtual environment NS2. Also we will be offered additional specific insights regarding this topic as required. Drop us with the specifics of your parameters, and we will support you in conducting a performance analysis. Contact us for information regarding your Network Load Balancing project utilizing the Ns2 tool, and we will assist you in generating innovative project ideas and topics.