Latency Analysis using Wireshark under Various Traffic Conditions

Network performance plays a crucial role in modern communication systems where data is transmitted continuously across networks. One of the most important parameters that defines network performance is latency, which refers to the delay experienced during data transmission.

In this post, latency is analyzed using Wireshark by capturing packets under different traffic conditions such as normal, low, medium, and high traffic. The goal is to observe how latency varies and how network performance is affected by increasing traffic load.

Objectives: 

To analyze network latency using Wireshark

To compare latency under normal, low, medium, and high traffic conditions

To understand the impact of traffic load on network performance

ARCHITECTURE

PROCEDURE: 


1. Wireshark was installed and the active network interface was selected.
2. Packet capture was started in Wireshark.
3. Traffic was generated using command prompt.
    Normal Traffic:
        ping google.com
    Low Traffic:
        ping -n 20 google.com
    Medium Traffic:
        ping -n 100 google.com
    High Traffic:
        ping -n 500 google.com
4. Each traffic condition was captured and saved as separate .pcap files.
5. ICMP packets were filtered using:
    icmp.type == 0
6. RTT (latency) was obtained using:
    icmp.resptime
7. TCP RTT was analyzed using:
    tcp.analysis.ack_rtt
8. Graphs were generated using:
    Statistics → IO Graph
9. Different graph types were created
10. Screenshots of all graphs were taken for analysis.

PICTURES & INFERENCES
NORMAL TRAFFIC
  • RTT remains low and stable
  • Very minimal variation observed

  • Indicates no network congestion


  • TCP RTT is consistent
  • No delay spikes present
  • Stable connection behavior

  • RTT values are uniform
  • No significant fluctuations

  • Efficient packet delivery

  • Smooth RTT pattern
  • No sudden changes
  • Stable network condition

  • Low packet count
  • Sparse transmission pattern
  • Minimal network load
    • LOW TRAFFIC
  • Slight increase in RTT variation
  • Occasional minor spikes
  • Still mostly stable compared to normal

  • Small RTT fluctuations appear
  • Slight delay variation
  • No congestion observed

  • RTT becomes slightly scattered
  • Minor irregular delays
  • Performance still stable

  • Small peaks visible
  • Slight instability compared to normal
  • Quick recovery after spikes

  • Increased packet count
  • More frequent transmissions
  • Slight rise in network activity
                                                                        MEDIUM TRAFFIC

  • Noticeable RTT spikes
  • Increased variability
  • Indicates moderate congestion

  • Periodic RTT peaks
  • Delay fluctuations increase
  • TCP performance affected

  • RTT widely distributed
  • Frequent spikes observed
  • Uneven packet delivery

  • Repeated peaks and drops
  • Higher instability
  • Dynamic network behavior

    • Higher packet density
    • Burst transmissions visible
    • Increased network load 
                                                                     HIGH TRAFFIC

  • Highly fluctuating RTT
  • No stable pattern
  • Strong congestion effects


    • Irregular RTT spikes
    • Delayed acknowledgments
    • Unstable TCP performance 

  • Highly scattered RTT values
  • Frequent delay spikes
  • Network instability visible

  • Sharp and large spikes
  • Severe delay variations
  • Congestion clearly present

  • Very high packet count
  • Continuous transmission
  • Heavy network load

    • As traffic increases from normal to high, RTT variation increases significantly, indicating reduced network stability and performance


        Trends Observed

    • Latency remains stable in normal traffic
    • Small fluctuations appear in low traffic
    • Medium traffic introduces noticeable spikes
    • High traffic shows heavy fluctuations and instability
    • Packet count increases with traffic load


        New Findings

    • Even low traffic introduces slight latency variation
    • TCP RTT provides more detailed analysis
    • Medium traffic shows burst-based congestion
    • High traffic causes non-linear delay increase

        Recommendations

    • Implement load balancing
    • Use traffic management techniques
    • Monitor RTT continuously
    • Optimize network routing
    • Apply QoS policies

    Use of AI in DA

    Artificial Intelligence tools were used to assist in understanding networking concepts, generating analysis steps, and interpreting graph results. AI also helped in drafting inferences and improving the overall structure and presentation of the assignment.

    Conclusion

    This experiment successfully analyzed network latency under different traffic conditions. It was observed that latency remains stable under low traffic but becomes unstable under high traffic due to congestion. The study highlights the importance of traffic management in maintaining network performance.

    YouTube Link : https://youtu.be/9SalbUeAM3U  

    GitHub Repository Link: https://github.com/Ashish01-starter/Computer_Networks/tree/Ashish01-starter-patch-1-Network_Analysis_using_latency

    References:

  • https://www.youtube.com/watch?v=1Rbx_LRPABM&t=344s
  • http://youtube.com/watch?v=0VO4z0_dHzI
  • https://www.ibm.com/think/topics/latency
  • https://www.gokinetic.com/blog/what-is-network-latency

    •  

            ACKNOWLEDGEMENT

              I would like to express my sincere gratitude to my parents for their continuous support and  encouragement. I also thank VIT SCOPE and university for providing the opportunity to carry  out this  Assignment as part of the current semester coursework.

              I extend my appreciation to the faculty and course instructors for their guidance throughout this assignment. I would also like to thank my friends and online resources for their valuable assistance and support.

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