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<p>Researchers have been working intensively on quality of service (QoS) and congestion control. There are discussions about bandwidth fairness, average throughput, average delay, and prioritizing traffic, but in depth packet analysis has been lacking. While troubleshooting Transmission Control Protocol (TCP) applications, it is crucial to check the number of packets marked. This work reports the QoS enabled network, where three applications, i.e., Telnet, Secure Shell (SSH), and Hypertext Transfer Protocol (HTTP), are configured, and their packets are marked using Access Control List (ACL), followed by a packet analysis. Additionally, troubleshooting commands are scripted using Python to reduce the effort and speed up the diagnosis. Prior to taking congestion control decisions, Industrialists will find this handy data incredibly helpful as it allows them to directly determine the number of marked packets. Work is distinctive in that it involves the application-wise monitoring of the number of packets on a network link &amp; integrating the troubleshooting with Python as a scope of improvement.</p>

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A novel technique for rapidly diagnosing a QoS-enabled network’s link congestion by calculating the size of TCP packets and automating the troubleshooting using a python script

  • 期刊论文

  • 发表日期：

    2025年3月19日

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  • Amit Sharma,
  • Anil Kumar,
  • Mahesh Kumar

摘要

Researchers have been working intensively on quality of service (QoS) and congestion control. There are discussions about bandwidth fairness, average throughput, average delay, and prioritizing traffic, but in depth packet analysis has been lacking. While troubleshooting Transmission Control Protocol (TCP) applications, it is crucial to check the number of packets marked. This work reports the QoS enabled network, where three applications, i.e., Telnet, Secure Shell (SSH), and Hypertext Transfer Protocol (HTTP), are configured, and their packets are marked using Access Control List (ACL), followed by a packet analysis. Additionally, troubleshooting commands are scripted using Python to reduce the effort and speed up the diagnosis. Prior to taking congestion control decisions, Industrialists will find this handy data incredibly helpful as it allows them to directly determine the number of marked packets. Work is distinctive in that it involves the application-wise monitoring of the number of packets on a network link & integrating the troubleshooting with Python as a scope of improvement.