Delay and Throughput Analysis

Oct 13, 2025 by magicalceylontours.com 9 views

Introduction

The rapid progress in communication technology has led to a significant increase in the use of telecommunications media, resulting in a substantial amount of data being transmitted through various network media. As the size of the data to be sent continues to grow, more frames are required to transfer the data, leading to a decrease in network performance. This is where data compression techniques play a crucial role, as they can reduce the size of large data files, thereby reducing the number of frames needed and increasing the efficiency of data transmission. In this article, we will discuss the effect of text data compression techniques on Ethernet frames on 802.11 network performance.

Analysis of Data and Network Compression Techniques 802.11

In a study that focuses on the influence of data compression techniques on network performance 802.11, the Huffman algorithm is used as a compression method for text data. The Huffman algorithm is an efficient compression technique, as it can reduce file size without losing important information. Through simulations carried out with netbeans 8.2 software, the test scenario is regulated to analyze the impact of compression on network performance, which is measured through three main metrics: delay, throughput, and congestion.

Delay and Throughput Analysis

The results of the study showed that the delay value increased after the application of compression as the length of the frame increased. For example, at the length of 80 bytes, the measured delay reached 1,017 ms, but this number jumped to 57.021 ms at the length of the 8000 frame byte. This shows that the greater the size of the frame, the time needed to transfer information is also longer, even though the data has been compressed.

In terms of throughput, the evaluation results recorded a significant increase after compression. At the length of 80 bytes, throughput recorded 8,011 bps, which increased dramatically to 172,738 bps when the length of the frame reached 8000 bytes. This improvement of throughput reflects the efficiency offered by compression techniques, which affect the quality and speed of data transmission.

Congestion Analysis

In addition, the congestion value also shows an increase that is in line with the length of the frame. Congestion values for 80-byte frame length recorded 0.000004804 seconds and increased to 0.000016874 seconds at the length of the 8000 frame byte. This increase is important to note, especially for network users who require data transmission in real-time.

Conclusion

Overall, data compression techniques affect network performance 802.11 in various aspects. With the application of the right compression algorithm, users can experience a better experience when using the network, because the amount of frame needed is reduced, although there are trade-offs in terms of delay. Readers are advised to consider the use of compression techniques in applications that can optimize data transmission, especially for large files, to improve network performance as a whole.

Future Research Directions

With further research, we can develop a better method to overcome the problem of network performance in facing the increasingly greater and diverse data challenges in this digital era. Some potential research directions include:

Developing new compression algorithms that can balance delay and throughput
Investigating the impact of compression on network performance in different scenarios, such as wireless networks and cloud computing
Exploring the use of compression techniques in conjunction with other network optimization methods, such as caching and routing

Recommendations for Practitioners

Based on the findings of this study, we recommend that practitioners consider the use of compression techniques in applications that require efficient data transmission, such as:

File transfer protocols (FTP)
Cloud storage services
Real-time data streaming applications

By applying compression techniques, practitioners can improve network performance, reduce delay, and increase throughput, ultimately leading to a better user experience.

Limitations of the Study

This study has several limitations that should be noted:

The study only focused on the Huffman algorithm and did not explore other compression techniques
The study only considered the impact of compression on network performance in a controlled environment and did not account for real-world network conditions
The study did not investigate the impact of compression on network security and reliability

Future Work

To address these limitations, future research should focus on:

Exploring the impact of different compression algorithms on network performance
Investigating the impact of compression on network performance in real-world scenarios
Examining the impact of compression on network security and reliability

Q: What is the main goal of data compression techniques in network performance?

A: The main goal of data compression techniques in network performance is to reduce the size of large data files, thereby reducing the number of frames needed and increasing the efficiency of data transmission.

Q: What is the Huffman algorithm, and how does it work?

A: The Huffman algorithm is a compression technique that works by assigning shorter codes to more frequently occurring characters in a data file. This allows for more efficient storage and transmission of the data.

Q: What are the three main metrics used to measure network performance in this study?

A: The three main metrics used to measure network performance in this study are:

Delay: The time it takes for data to be transmitted over the network.
Throughput: The amount of data that can be transmitted over the network in a given time.
Congestion: The amount of traffic on the network, which can affect the speed and reliability of data transmission.

Q: What were the results of the study in terms of delay and throughput?

A: The results of the study showed that the delay value increased after the application of compression as the length of the frame increased. However, the throughput value increased significantly after compression, indicating that the compression technique was effective in reducing the amount of data that needed to be transmitted.

Q: What are the implications of the study for network users?

A: The study suggests that network users can experience a better experience when using the network by applying compression techniques, which can reduce the amount of frame needed and increase the efficiency of data transmission.

Q: What are some potential research directions for future studies?

A: Some potential research directions for future studies include:

Developing new compression algorithms that can balance delay and throughput
Investigating the impact of compression on network performance in different scenarios, such as wireless networks and cloud computing
Exploring the use of compression techniques in conjunction with other network optimization methods, such as caching and routing

Q: What are some recommendations for practitioners based on the study?

A: Based on the study, practitioners are recommended to consider the use of compression techniques in applications that require efficient data transmission, such as file transfer protocols (FTP), cloud storage services, and real-time data streaming applications.

Q: What are some limitations of the study?

A: The study has several limitations, including:

The study only focused on the Huffman algorithm and did not explore other compression techniques
The study only considered the impact of compression on network performance in a controlled environment and did not account for real-world network conditions
The study did not investigate the impact of compression on network security and reliability

Q: What are some potential future work directions to address the limitations of the study?

A: Some potential future work directions to address the limitations of the study include:

Exploring the impact of different compression algorithms on network performance
Investigating the impact of compression on network performance in real-world scenarios
Examining the impact of compression on network security and reliability

Q: What are some potential applications of the study's findings?

A: The study's findings have potential applications in various fields, including:

Cloud computing: Compression techniques can be used to reduce the amount of data that needs to be transmitted over the cloud, improving the efficiency and reliability of cloud-based services.
Wireless networks: Compression techniques can be used to reduce the amount of data that needs to be transmitted over wireless networks, improving the efficiency and reliability of wireless-based services.
Real-time data streaming: Compression techniques can be used to reduce the amount of data that needs to be transmitted in real-time data streaming applications, improving the efficiency and reliability of these applications.