Introduction
This blog presents the analysis of network performance parameters using Wireshark under different traffic conditions such as low, normal, and high traffic. The study focuses on key metrics including throughput, latency, jitter, packet loss, and attenuation.
The objective is to understand how network behavior changes under varying load conditions and to analyze the efficiency and reliability of data transmission.
Objectives
• To analyze throughput, latency, jitter, and packet loss under different traffic conditions.
• To compare network performance in low, normal, and high traffic scenarios.
• To study the effect of attenuation on network parameters.
Tools used
• Wireshark (for packet capture and analysis)
• Python (for data processing and graph generation)
• Matplotlib & Pandas libraries
Architecture
The diagram represents the end-to-end process of network analysis, starting from traffic generation and packet capture using Wireshark. The captured data is exported and analyzed using Python to compute key network parameters. The processed data is visualized through graphs, which help in evaluating network performance under varying traffic conditions.
Procedure
1. Captured network traffic using Wireshark under different traffic conditions.
2. Exported captured data into CSV format.
3. Processed data using Python scripts.
4. Generated graphs for throughput, latency, jitter, and packet loss.
5. Analyzed results and compared performance.
Low Traffic Analysis
Inference:
• The throughput remains relatively stable due to low network load.
• Latency is low and consistent with minimal variation.
• Jitter values are small, indicating stable packet delivery.
• No significant packet loss observed.
• Overall network performance is efficient and reliable.
Normal Traffic Analysis
Inference:
• Throughput shows moderate fluctuations due to increased traffic.
• Latency increases slightly compared to low traffic.
• Jitter shows occasional spikes indicating variation in delay.
• Network performance is stable but less consistent than low traffic.
• Minor congestion effects are observed.
High Traffic Analysis
Inference:
• Throughput fluctuates significantly due to heavy congestion.
• Latency increases sharply indicating network delays.
• Jitter shows high variation due to inconsistent packet delivery.
• Packet loss increases during peak traffic.
• Network performance degrades under high load conditions.
Attenuation Analysis inference:
• Throughput decreases as attenuation increases
• Goodput reduces faster due to retransmissions
• Latency increases linearly with attenuation
• Jitter increases due to signal degradation
• High attenuation leads to poor network performance
New Findings and Recommendations
• Network performance is highly dependent on traffic load.
• High traffic leads to congestion and packet loss.
• Attenuation negatively impacts signal quality.
• Efficient traffic management improves performance.
• Proper bandwidth allocation is necessary for stability.
Use of AI
AI tools were used to assist in structuring the analysis, refining explanations, and improving presentation quality. Python was also used for automated data analysis and visualization.
Conclusion
This study demonstrates that network performance degrades as traffic intensity and attenuation increase. Low traffic conditions provide stable and efficient communication, while high traffic leads to congestion, delay, and packet loss. Proper optimization techniques are essential to maintain reliable network performance.
Video Link
https://www.youtube.com/watch?v=saMdbieoiDE
GitHub Repository
The complete source code used for data processing, analysis, and graph generation is available in the following GitHub repository:
https://github.com/mohit2024vitstudent/CN_BCSE308P_LAB_RECORDS
References
• Wireshark Official Documentation
• Online tutorials and network analysis resources
Acknowledgement
I would like to thank my faculty and institution for their guidance and support. I also acknowledge the use of online resources and tools that helped in completing this assignment.
Author
Author: Mohit Sangwan
Course: Computer Networks Lab (BCSE308P)
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