How to Calculate Network Mean Slow Down in omnet++

To calculate Network Mean Slowdown in OMNeT++ has needs to evaluate how much the performance of network tasks like file transfers, packet processing, or message passing is slowed down because of network congestion, delays, or other factors compared to their ideal execution time. The Mean Slowdown metric is particularly useful in evaluating the fairness and efficiency of resource allocation in a network.

Steps to Calculate Network Mean Slowdown in OMNeT++

1. Define Slowdown:
   • Slowdown for a task like a packet transfer is commonly calculated as the ratio of the actual time taken to finishes the task to the ideal or minimum time that the task would take if there were no delays or congestion.
   • For a single task: Slowdown=Actual Completion TimeIdeal Completion Time\text{Slowdown} = \frac{\text{Actual Completion Time}}{\text{Ideal Completion Time}}Slowdown=Ideal Completion TimeActual Completion Time​
2. Track Actual and Ideal Completion Times:
   • During the simulation, track both the actual time it takes for each task to complete and the ideal time it would take under optimal conditions.
   • Actual Completion Time is the time difference among when a task is started and when it is completed.
   • Ideal Completion Time is the time it would take under no load or perfect conditions is commonly based on the minimum transmission delay.

simtime_t actualStartTime;

simtime_t actualCompletionTime;

simtime_t idealCompletionTime;

1. Calculate Slowdown for Each Task:
   • For each task like packet transmission then estimate the slowdown.

simtime_t slowdown = actualCompletionTime / idealCompletionTime;

1. Track Slowdown for All Tasks:
   • Accumulate the slowdown for each task and track the number of tasks to estimate the mean slowdown later.

simtime_t totalSlowdown = 0;

int taskCount = 0;

1. • For each completed task:

void onTaskCompleted(simtime_t actualStartTime, simtime_t actualCompletionTime, simtime_t idealCompletionTime) {

simtime_t slowdown = (actualCompletionTime – actualStartTime) / idealCompletionTime;

totalSlowdown += slowdown;

taskCount++;

}

1. Calculate Mean Slowdown:
   • After all tasks have been processed, evalaute the mean slowdown.

double meanSlowdown = totalSlowdown.dbl() / taskCount;

1. Record the Mean Slowdown:
   • Use the recordScalar function to save the mean slowdown value for later analysis.

recordScalar(“Mean Slowdown”, meanSlowdown);

EV << “Mean Slowdown: ” << meanSlowdown << endl;

Example Implementation in OMNeT++

The below is the sample of how to execute the calculation of network mean slowdown in an OMNeT++ module:

class SlowdownCalculator : public cSimpleModule {

private:

simtime_t totalSlowdown;

int taskCount;

protected:

virtual void initialize() override {

totalSlowdown = 0;

taskCount = 0;

}

virtual void handleMessage(cMessage *msg) override {

cPacket *pkt = check_and_cast<cPacket*>(msg);

// Assume we have recorded the actual start and completion times

simtime_t actualStartTime = pkt->par(“actualStartTime”);

simtime_t actualCompletionTime = simTime();  // Current time is the completion time

// Assume the ideal completion time is stored or calculated

simtime_t idealCompletionTime = pkt->par(“idealCompletionTime”);

// Calculate slowdown

simtime_t slowdown = (actualCompletionTime – actualStartTime) / idealCompletionTime;

totalSlowdown += slowdown;

taskCount++;

EV << “Task Slowdown: ” << slowdown << endl;

delete pkt;  // Clean up the packet

}

virtual void finish() override {

if (taskCount > 0) {

double meanSlowdown = totalSlowdown.dbl() / taskCount;

recordScalar(“Mean Slowdown”, meanSlowdown);

EV << “Mean Slowdown: ” << meanSlowdown << endl;

}

}

};

Explanation:

1. Tracking Time and Calculating Slowdown:
   • The module tracks the actual start and completion times of tasks like packet transmissions and compares these with an ideal completion time to estimate the slowdown for each task.
2. Accumulate Slowdown Values:
   • The total slowdown across all tasks is accumulated, and the number of tasks is counted.
3. Calculate Mean Slowdown:
   • At the end of the simulation (in the finish() method), the mean slowdown is estimated by dividing the total slowdown by the number of tasks.
4. Recording and Analysis:
   • The mean slowdown is recorded and can be measured post-simulation to assess the effect of network conditions on task performance.

In conclusion, we had executed the Mean Slowdown in OMNeT++ that has analysed the performance of network task. We also offer more information regarding the execution of the Mean Slowdown in alternative simulation tools.

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