https://github.com/kishan-25/solving-bufferbloat-using-multilevel-feedback-queues-mlfq-

Developed an advanced Multilevel Feedback Queueing (MLFQ) algorithm to mitigate network performance issues like latency, jitter, and bufferbloat. Optimized packet prioritization for real-time applications, improving throughput, latency, and bandwidth fairness, with validated results demonstrating enhanced network efficiency.
https://github.com/kishan-25/solving-bufferbloat-using-multilevel-feedback-queues-mlfq-

algortithms cpp ns3-simulator

Last synced: about 1 year ago
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Developed an advanced Multilevel Feedback Queueing (MLFQ) algorithm to mitigate network performance issues like latency, jitter, and bufferbloat. Optimized packet prioritization for real-time applications, improving throughput, latency, and bandwidth fairness, with validated results demonstrating enhanced network efficiency.

• Host: GitHub
• URL: https://github.com/kishan-25/solving-bufferbloat-using-multilevel-feedback-queues-mlfq-
• Owner: kishan-25
• License: other
• Created: 2024-09-13T09:13:50.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2025-02-19T17:27:35.000Z (over 1 year ago)
• Last Synced: 2025-02-19T18:31:05.401Z (over 1 year ago)
• Topics: algortithms, cpp, ns3-simulator
• Homepage:
• Size: 161 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Solving Bufferbloat using Multilevel Feedback Queues (MLFQ)

## Installation

The research paper is not published yet, so the code is not available.

## Project Overview

This project implements a **Multilevel Feedback Queue (MLFQ)** algorithm to mitigate the **bufferbloat** problem in networking. The MLFQ prioritizes packet scheduling based on real-time requirements, ensuring low latency for critical applications like video conferencing, online gaming, and streaming services.

## Overview

Bufferbloat occurs due to excessive packet queuing in oversized network buffers, leading to increased **latency, jitter, and packet loss**. This project presents an MLFQ-based scheduling mechanism that prioritizes packets dynamically to enhance **Quality of Service (QoS)** in networks.

## Features

- **Priority-based scheduling**: Categorizes packets into different priority queues.

- **Real-time adaptation**: Uses a feedback mechanism to promote long-waiting packets.

- **Latency Reduction**: Minimizes delay for real-time applications.

- **Efficient Bandwidth Utilization**: Ensures fair distribution of bandwidth.

- **Simulation of Network Traffic**: Models packet scheduling in a simulated environment.

## Usage

Modify the `config.py` file to customize simulation parameters like packet arrival rates, queue size, and priority levels. Then, execute `main.py` to analyze MLFQ performance.

## Algorithm Details

### 1. **Packet Classification**

   - Video conferencing packets (UDP, ports 16384-32767) → **Highest Priority**

   - Online gaming packets (UDP, ports 3478-3480) → **High Priority**

   - Streaming packets (TCP, keyword 'video') → **Medium Priority**

   - File downloads (Other TCP/UDP traffic) → **Lowest Priority**

### 2. **MLFQ Structure**

   - **Level 0:** Real-time critical packets (video calls, gaming)

   - **Level 1:** Streaming packets

   - **Level N:** Non-time-sensitive traffic (downloads, background processes)

### 3. **Feedback Mechanism**

   - If a packet waits too long in a lower-priority queue, it gets promoted to a higher-priority queue to prevent starvation.

## Results

The simulation evaluates MLFQ efficiency using the following metrics:

- **Packet processing time**

- **Queue wait times**

- **Dropped packets due to congestion**

- **Improved latency for real-time applications**

## Future Work

- Implement **machine learning** for dynamic traffic classification.

- Optimize **aging mechanisms** to balance fairness and efficiency.

- Extend support for **Software-Defined Networking (SDN)** environments.

## References

Relevant research papers and sources used in this project:

- J. Gettys and K. Nichols, "Bufferbloat: Dark buffers in the Internet," Communications of the ACM, 2012.

- K. Nichols and V. Jacobson, "Controlling queue delay," ACM, 2012.

- Various studies on SFQ, CoDel, and FQ-CoDel techniques.

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### Author

Developed by **Balkishan**