Throughput Evaluation Of Link Layer Flow Control

Throughput Evaluation of Link Layer Flow Control

Determine the average throughput of the overall link between nodes A and C in a network where frames are generated at node A and sent through node B to node C. The simulation should be performed using OMNeT++, Matlab, or C outside of OMNeT++, considering specific parameters such as data rates, protocols, delays, and transmission probabilities. The process involves varying transmission probability (TxP) from 0.1 to 1.0 with a step of 0.1, running sufficient simulations for each TxP to gather reliable throughput data, and then analyzing and plotting throughput results for two different data rate scenarios (50 kbps and 150 kbps at nodes B and C). The final submission should include a throughput table, plot of throughput vs. TxP, a brief narrative discussion, source code with inline comments, and all related materials.

Paper For Above instruction

In modern computer networks, understanding the throughput between nodes is essential for optimizing data transmission and ensuring efficient communication protocols. This paper evaluates the end-to-end throughput of a specific network topology involving three nodes—A, B, and C—where frames are generated at node A, relayed through node B, and received at node C. The goal is to quantify how different transmission probabilities and data rates impact overall network performance, utilizing simulation models to obtain accurate measurements.

The network setup involves data frames of 1000 bits generated at node A at a constant data rate of 100 kbps, with each frame having a certain probability (TxP) of being transmitted. Frames that are not transmitted are discarded immediately, and the next frame is generated after the current frame's transmission time. The links between nodes are duplex, with propagation delays of 5 µs/km, ensuring minimal latency impacts in the simulation. Importantly, the network employs different protocols: a sliding window protocol with a window size of 7 between nodes A and B, and a stop-and-wait protocol between nodes B and C, which significantly influences throughput due to their differing mechanisms.

Simulation parameters are crucial to the analysis—specifically, the data rates at nodes B and C, which are set initially at 50 kbps, and later increased to 150 kbps to compare effects. For each data rate setting, TxP is varied in steps of 0.1 from 0.1 to 1.0. Multiple simulation runs are performed at each point to ensure statistical reliability. The simulations measure the end-to-end throughput, which is then tabulated and plotted for visualization. This approach provides insights into the effect of transmission probability and data rates on overall network performance.

Impact of Transmission Probability (TxP) on Throughput

As TxP increases, the likelihood of frames being successfully transmitted also increases, leading to higher throughput. However, beyond a certain point, the effects of protocol layer overheads, buffer limitations, and protocol inefficiencies become evident. For instance, at low TxP values, many frames are discarded, resulting in lower throughput, whereas at higher TxP values, the network approaches optimal utilization but may experience congestion if buffers are limited or if the protocol cannot handle the increased load efficiently. The simulations demonstrate that maximum throughput is achieved when TxP is near 1.0 but is dependent on the data rate of the links—higher data rates at nodes B and C facilitate better throughput at corresponding probability levels.

Effect of Data Rate Changes at Nodes B and C

Increasing the data rate from 50 kbps to 150 kbps at nodes B and C results in a significant improvement in overall throughput across all TxP values. This enhancement is due to the increased capacity of the links, which reduces bottlenecks and allows for more frames to be transmitted successfully in a given timeframe. The simulation results indicate a shift in the throughput curve upward as data rates increase, confirming that higher link capacities amplify the benefits of higher transmission probabilities. This finding underscores the importance of adjusting link capacities and transmission control algorithms to optimize network performance.

Conclusion

The simulation-based evaluation underscores the complex interaction between transmission probability, link capacity, and protocol efficiency in determining network throughput. While increasing TxP generally improves throughput, the benefits plateau near full transmission probability due to protocol limitations and network dynamics. Higher data rates substantially enhance throughput, especially at high TxP values. These insights illustrate the importance of adaptive flow control and capacity planning in network design to maximize performance and reliability.

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