Analysis of Round Trip Time (RTT) under Different Traffic Conditions using Wireshark

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Introduction

Network performance depends on several parameters such as delay, bandwidth, throughput, and packet loss. Among these, delay plays a significant role in determining how efficiently data is transferred between communicating devices. One of the most important delay-related metrics is Round Trip Time (RTT).

RTT directly influences the behaviour of transport-layer protocols, especially the Transmission Control Protocol (TCP). TCP relies on acknowledgments to ensure reliable data transmission. The sender cannot continuously transmit unlimited data; it must wait for acknowledgments before sending additional packets. Therefore, RTT has a direct impact on data transfer speed.

RTT is measured as the time difference between when a packet is sent and when its acknowledgment is received.

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Objectives

• To analyze Round Trip Time (RTT) in TCP communication

• To study network performance under different traffic conditions

• To observe the impact of RTT on throughput and packet loss

• To analyze TCP behavior using Wireshark

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Reference from which the DA was started

• https://sharkfest.wireshark.org/retrospective/sfus/presentations19/28-37.pdf

• https://www.youtube.com/watch?v=1eJHqyyjHqk

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Description of the Source

The Sharkfest presentation provided a detailed understanding of TCP performance analysis using Wireshark. It explained how RTT, packet loss, and throughput can be analyzed using captured packets. The YouTube resource helped in understanding practical implementation and visualization of network traffic.

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Architecture of the Work

Description:
The setup consists of a client system communicating with a server over a network. Wireshark is used to capture and analyze packets at the client side. Different traffic conditions are generated using command-line tools.

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Procedure

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Low Traffic

Command used:
ping -n 20 google.com

Observation:
Low number of packets transmitted, resulting in low RTT and efficient communication.

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Medium Traffic

Command used:
ping -n 100 google.com

Observation:
Increased packet transmission leads to moderate RTT and slight variations in delay.

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High Traffic

Command used:
ping -n 500 google.com

Observation:
Heavy traffic leads to congestion, increased RTT, packet delay, and possible packet loss.

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Outputs

(RTT Analysis)

The Round Trip Time (RTT) values were observed under different traffic conditions using Wireshark. RTT represents the time taken for a packet to travel from the sender to the receiver and back.

In low traffic conditions, RTT remained low with slight fluctuations due to limited packet transmission.

During medium traffic conditions, RTT values showed noticeable variation as the number of packets increased, indicating moderate network load.

In high traffic conditions, RTT values increased significantly with large fluctuations, reflecting network congestion and delay in packet acknowledgment.

Overall, it was observed that RTT varies with traffic intensity and increases as network load increases.

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Inferences on Network Parameters

Graph 1: RTT vs Time (Low Traffic)

The RTT graph under low traffic conditions shows very small and stable values over time. There are minimal fluctuations, indicating that packets are transmitted and acknowledged quickly without delay. This reflects efficient network performance with negligible congestion and low latency.

Graph 2: RTT vs Time (Medium Traffic)

In the medium traffic RTT graph, slight variations in RTT values are observed. The delay increases compared to low traffic, and occasional spikes indicate temporary congestion. However, the overall trend remains fairly stable, showing that the network can still handle moderate load efficiently.

Graph 3: RTT vs Time (High Traffic)

This suggests that the network is experiencing congestion, leading to reduced performance and higher latency

Graph 4: Throughput vs Time (Low Traffic)

Throughput is low but stable since fewer packets are being transmitted. There is no congestion, so the network delivers data efficiently but at a lower rate.

Graph 5: Throughput vs Time (Medium Traffic)

Throughput increases and becomes more optimal. The network resources are utilized efficiently, resulting in higher data transfer rates with minimal packet loss or delay.

Graph 6: Throughput vs Time (HighTraffic)

Throughput initially increases but may fluctuate or even degrade due to congestion. Packet loss, delays, and retransmissions can reduce effective throughput, leading to instability in performance.

Graph 7: Packet Count vs Time (low traffic)

Packet count increases slowly over time with a nearly flat and stable curve. This indicates fewer packets are being transmitted, and the network is underutilized with no congestion.

Graph 8: Packet Count vs Time (medium traffic)

Packet count increases at a steady rate with moderate fluctuations. The graph shows a balanced flow of packets, indicating efficient utilization of network resources without major delays.

Graph 9: Packet Count vs Time (high traffic)

Packet count shows sharp spikes and sudden increases over time. The graph may appear uneven due to bursts of packet transmission, indicating heavy load, congestion, and possible packet drops or retransmissions.

Graph 10: TCP Window Size vs Time (low traffic)

The TCP window size remains relatively small and stable over time. Since there is no congestion, the sender does not need to aggressively increase the window size. The graph shows a smooth and steady pattern.

Graph 11: TCP Window Size vs Time (medium traffic)

The TCP window size gradually increases with time, showing controlled growth. There may be slight fluctuations due to minor network variations, but overall it reflects efficient congestion control and good utilization of available bandwidth.

Graph 12: TCP Window Size vs Time (high traffic)

The TCP window size shows sharp increases followed by sudden drops. This behavior is due to congestion control mechanisms (like slow start and congestion avoidance). When packet loss occurs, the window size decreases, then increases again, creating a fluctuating pattern.

Graph 13: Sequence Number  (Low Traffic)

The sequence number increases slowly and linearly with time because packets are transmitted at a lower rate. The graph is smooth, indicating continuous and orderly packet transmission without retransmissions.

Graph 14: Sequence Number  (Medium Traffic)

The sequence number increases more rapidly compared to low traffic. The graph remains mostly linear with small variations, showing that more packets are being sent while maintaining stable communication.

Graph 15: Sequence Number  (High Traffic)

The sequence number rises quickly with time but may show irregularities or repeated values. These fluctuations indicate retransmissions due to congestion or packet loss. The graph appears steeper because a larger amount of data is being transmitted.

Graph 16: Byte Count vs Time

The byte count increases slowly over time with a smooth and gradual slope. This indicates that only a small amount of data is being transmitted, and the network is underutilized with no congestion.

Graph 17: Byte Count vs Time (medium traffic)

The byte count increases at a steady and faster rate compared to low traffic. The graph shows a consistent upward trend, indicating efficient data transmission and good utilization of network bandwidth.

Graph 18: Byte Count vs Time (high traffic)

The byte count increases rapidly with a steep slope, but may show fluctuations or uneven growth. This suggests heavy data transfer along with possible congestion, packet loss, or retransmissions affecting smooth transmission.

Graph 19: Packets/1sec (medium traffic)

Packets per second increase to a moderate level with slight fluctuations. The graph shows a steady pattern, indicating efficient and balanced packet flow without major delays.

Graph 20: Packets/1sec (high traffic)

Packets per second are high with noticeable spikes and drops. The graph fluctuates due to heavy load, indicating congestion, burst transmissions, and possible packet loss or retransmissions.

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New Findings and Recommendations

• RTT increases significantly under high traffic conditions

• High RTT leads to reduced throughput

• Packet loss increases during congestion

• TCP reduces its window size under heavy load

• Efficient bandwidth management is required

• Use congestion control mechanisms to improve performance

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Use of AI in this DA

Artificial Intelligence tools were used to structure the report, analyze network behavior, and interpret Wireshark graphs. AI assisted in organizing the experimental data and presenting it in a clear and structured format.

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Conclusion

This study highlights the importance of RTT in determining network performance. As traffic increases, RTT and packet delays increase, leading to reduced throughput and efficiency. Wireshark proves to be an effective tool for analyzing TCP performance under varying traffic conditions.

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YouTube Video Link

https://youtu.be/KjDC43hFDds

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GitHub Repository Link

https://github.com/shreyashree0207/Analysis-of-Round-Trip-Time-RTT-under-Different-Traffic-Conditions-using-Wiresharks

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References

• Sharkfest Wireshark Presentation

• YouTube Tutorial on Wireshark

• Wireshark Official Documentation

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Acknowledgement

• I sincerely express my gratitude to the School of Computer Science and Engineering (SCOPE) at the Vellore Institute of Technology, Chennai, for offering the theory and laboratory courses in Computer Networks during the Winter Semester 2025–2026 with an industry-standard and well-structured curriculum.
• I extend my heartfelt thanks to my course faculty, Dr. T. Subbulakshmi, Professor, SCOPE, VIT Chennai, for her valuable guidance, continuous support, and insightful teaching throughout the course.
• I would like to acknowledge Gerald Combs, the founder of Wireshark and recipient of the ACM Software System Award (2018), for developing an exceptional tool that greatly facilitated network traffic analysis and enhanced practical learning.
• I also thank my peers for their support, collaboration, and valuable suggestions, which contributed significantly to my learning experience.
• A special note of appreciation goes to my friend (Garv Goyal), who guided and supported me in understanding key concepts and practical aspects at various stages of this work.
• I express my sincere gratitude to my parents, siblings, and relatives for their constant encouragement, motivation, and unwavering support.
• Lastly, I would like to acknowledge all the books, online resources, and individuals whose contributions, though not individually mentioned, have supported me in completing this work successfully.

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Author

Shreyashree Tiwari, II year B.Tech. CSE Student ,School of Computer Science and Engineering , VIT Chennai