How to Calculate Network Congestion in omnet++
To calculate the network congestion in OMNeT++ has requires to include monitoring various network metrics like throughput, packet drop rates, queue lengths, and delay. Congestion happens when the network’s demand exceeds its capacity, leading to performance degradation, especially in terms of increased delays and packet losses. Below is a procedure to calculate and analyse network congestion in OMNeT++:
Step-by-Step Implementations:
- Identify Congestion Indicators:
- Queue Length: Chase the length of queues at routers or switches. An increasing queue length is a main indicator of congestion.
- Packet Drop Rate: Observe the number of packets dropped due to full queues. High packet drop rates are a sign of congestion.
- End-to-End Delay: Calculate the time taken for a packet to travel from source to destination. Increased delays frequently show congestion.
- Throughput: Track the rate at which data is effectively transmitted through the network. A decrease in throughput can specify congestion.
- Configure the Network for Monitoring:
- Use proper network modules like Queue, Router, and Link in the OMNeT++ simulation to set up the network where congestion can occur.
Example NED Configuration:
simple Router {
gates:
in[]; // Incoming connections
out[]; // Outgoing connections
submodules:
queue: DropTailQueue {
parameters:
queueCapacity = 100; // Queue size
}
}
- In the above example, a DropTailQueue is used in a Router module, where packets can be released if the queue is full, indicating congestion.
- Track Queue Length and Packet Drops:
- Observe the queue length and packet drops at each router or switch in the network.
Example in C++:
- We can extend or change the queue handling logic to monitor these metrics.
virtual void handleMessage(cMessage *msg) override {
if (auto pkt = check_and_cast<Packet *>(msg)) {
if (queue.length() < queueCapacity) {
queue.insert(pkt);
} else {
numPacketsDropped++;
delete pkt;
}
}
}
virtual void finish() override {
recordScalar(“Queue Length”, queue.length());
recordScalar(“Packets Dropped”, numPacketsDropped);
}
- Measure End-to-End Delay:
- To calculate the delay, record the time when a packet is received and when it is send at the end. The variance gives the end-to-end delay.
Example in C++:
virtual void handleMessage(cMessage *msg) override {
if (auto pkt = dynamic_cast<Packet *>(msg)) {
simtime_t sendTime = pkt->getCreationTime();
simtime_t receiveTime = simTime();
simtime_t delay = receiveTime – sendTime;
recordScalar(“End-to-End Delay”, delay.dbl());
}
}
- Monitor Throughput:
- Throughput can be measured by monitoring the amount of data effectively transmitted over a period of time.
Example in C++:
virtual void handleMessage(cMessage *msg) override {
if (auto pkt = dynamic_cast<Packet *>(msg)) {
throughput += pkt->getByteLength();
}
}
virtual void finish() override {
recordScalar(“Throughput”, throughput / simTime().dbl());
}
- Analyse Congestion:
- Congestion Level: Based on the metrics gathered, we can measure a congestion level. For example, if the queue length overdoes a certain threshold, we can consider the network congested.
- Congestion Ratio: Estimate the ratio of dropped packets to total packets sent as a measure of congestion.
double congestionRatio = (double)numPacketsDropped / totalPacketsSent;
recordScalar(“Congestion Ratio”, congestionRatio);
- Configuration in omnetpp.ini:
- Make sure the simulation is configured to log the required metrics.
Example Configuration:
*.router[*].queue.recordScalar(“Queue Length”);
*.router[*].queue.recordScalar(“Packets Dropped”);
*.router[*].**.recordScalar(“End-to-End Delay”);
*.router[*].**.recordScalar(“Throughput”);
- Simulation and Analysis:
- Analyse the recorded metrics to identify periods of congestion after running the simulation. We can visualize these metrics using OMNeT++’s built-in tools or export them for more analysis.
- Advanced Congestion Metrics:
- Average Queue Length: Over time, average the queue length to know usual congestion levels.
- Link Utilization: Calculate the utilization of network links, as high utilization is frequently a precursor to congestion.
Example in omnetpp.ini:
*.router[*].queue.recordScalar(“Average Queue Length”);
*.router[*].linkUtilization.recordScalar(“Link Utilization”);
By watching and considering these metrics, we can successfully measure and understand network congestion in the OMNeT++ simulations, permitting for better network design and optimization.
Throughout this paper, we demonstrate Network Congestion that observing numerous network metrics like throughput, packet drop rates, queue lengths, and delay. We see the procedure to calculate and analyse Network congestion in OMNeT++. We will give further data following your needs.
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