How to Calculate Network Load Balancing in omnet++

To calculate the network load balancing in OMNeT++ includes assessing how evenly the traffic or workload is scattered across the network. Efficient load balancing make sure that no single link or node is overwhelmed while others are underutilized, which helps in enhancing the network’s performance and preventing bottlenecks.

Step-by-Step Implementations:

Define Load Balancing Metrics

Earlier computing load balancing, we want to describe the metrics that will be used to evaluate it. Common metrics contain:

Node Load: The amount of traffic or workload managed by each node.
Link Utilization: The utilization level of each network link.
Standard Deviation of Node Loads: Calculates the variability in the load across all nodes.
Coefficient of Variation (CoV): A normalized measure of load variation across nodes, assessed as the ratio of the standard deviation to the mean.

Set up Load Tracking

In the network model, execute mechanisms to track the load on each node or link. This might encompass counting the number of packets processed, the amount of data transmitted, or the processing time used by each node.

Example: Tracking Node Load

class Node : public cSimpleModule {

private:

int packetsProcessed = 0;

double dataProcessed = 0.0; // Total data processed in bytes

protected:

virtual void handleMessage(cMessage *msg) override {

packetsProcessed++;

dataProcessed += msg->getByteLength();

// Process and forward the packet

send(msg, “out”);

}

virtual void finish() override {

recordScalar(“Packets Processed”, packetsProcessed);

recordScalar(“Data Processed (bytes)”, dataProcessed);

}

};

Measure and Collect Load Data

As the simulation runs, gather the load data for each node or link. This data will be used to measure the load distribution across the network.

Calculate Load Balancing Metrics

Once we have gathered the load data, we can compute several load balancing metrics.

Example: Calculating Standard Deviation and Coefficient of Variation

#include <cmath>

#include <vector>

double calculateStandardDeviation(const std::vector<double>& loads, double mean) {

double sumSquaredDifferences = 0.0;

for (double load : loads) {

sumSquaredDifferences += std::pow(load – mean, 2);

}

return std::sqrt(sumSquaredDifferences / loads.size());

}

double calculateCoefficientOfVariation(double stddev, double mean) {

return (mean == 0.0) ? 0.0 : stddev / mean;

}

void calculateLoadBalancingMetrics() {

std::vector<double> nodeLoads = getNodeLoads(); // Retrieve loads from all nodes

double totalLoad = 0.0;

for (double load : nodeLoads) {

totalLoad += load;

}

double meanLoad = totalLoad / nodeLoads.size();

double stddev = calculateStandardDeviation(nodeLoads, meanLoad);

double cov = calculateCoefficientOfVariation(stddev, meanLoad);

EV << “Mean Load: ” << meanLoad << endl;

EV << “Standard Deviation of Load: ” << stddev << endl;

EV << “Coefficient of Variation: ” << cov << endl;

recordScalar(“Mean Load”, meanLoad);

recordScalar(“Load Standard Deviation”, stddev);

recordScalar(“Load Coefficient of Variation”, cov);

}

Implement and Test Load Balancing Algorithms

If the network exhibits poor load balancing, we might need to execute load balancing algorithms to distribute the load more evenly. Common strategies contain:

Round-Robin: Distribute incoming traffic consistently across nodes.
Least-Connection: Forward traffic to the node with the least number of active connections.
Weighted Load Balancing: Allot weights to nodes or links based on their capacity, and distribute traffic accordingly.

Example: Simple Round-Robin Load Balancing

int currentNode = 0;

void handleMessage(cMessage *msg) override {

int nextNode = currentNode;

currentNode = (currentNode + 1) % gateSize(“out”); // Move to the next node in round-robin

send(msg, “out”, nextNode);

}

Analyse Load Distribution

After running the simulation with load balancing, investigate the distribution of load across the network. The key metrics to consider are:

Evenness of Load Distribution: How evenly the load is distributed across nodes and links.
Reduction in Bottlenecks: Whether the load balancing algorithm decreases congestion on any specific node or link.

Compare with Baseline

Compare the load balancing results with a baseline scenario without load balancing to assess the efficiency of the load balancing strategy.

Example Scenario

Given below is an example of calculating network load balancing in OMNeT++:

class NetworkNode : public cSimpleModule {

private:

int packetsProcessed = 0;

double dataProcessed = 0.0; // Total data processed in bytes

protected:

virtual void handleMessage(cMessage *msg) override {

packetsProcessed++;

dataProcessed += msg->getByteLength();

// Process and forward the packet

send(msg, “out”);

}

virtual void finish() override {

recordScalar(“Packets Processed”, packetsProcessed);

recordScalar(“Data Processed (bytes)”, dataProcessed);

}

void calculateLoadBalancingMetrics() {

std::vector<double> nodeLoads = getNodeLoads();

double totalLoad = 0.0;

for (double load : nodeLoads) {

totalLoad += load;

}

double meanLoad = totalLoad / nodeLoads.size();

double stddev = calculateStandardDeviation(nodeLoads, meanLoad);

double cov = calculateCoefficientOfVariation(stddev, meanLoad);

EV << “Mean Load: ” << meanLoad << endl;

EV << “Standard Deviation of Load: ” << stddev << endl;

EV << “Coefficient of Variation: ” << cov << endl;

recordScalar(“Mean Load”, meanLoad);

recordScalar(“Load Standard Deviation”, stddev);

recordScalar(“Load Coefficient of Variation”, cov);

}

};

Post-Simulation Analysis

To analyse the load balancing metrics gathered during the simulation using OMNeT++’s analysis tools. This analysis will support we understand how successfully the load is distributed across the network and whether the load balancing strategy is working as intended.

In conclusion, we had executed comprehensive details to execute and calculate the Network load balancing by using OMNeT++. More informations will be presented this topic in various tools.

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