{"id":27163849,"url":"https://github.com/kishan-25/solving-bufferbloat-using-multilevel-feedback-queues-mlfq-","last_synced_at":"2025-04-09T01:51:49.344Z","repository":{"id":275803457,"uuid":"856821093","full_name":"kishan-25/Solving-Bufferbloat-using-Multilevel-Feedback-Queues-MLFQ-","owner":"kishan-25","description":"Developed an advanced Multilevel Feedback Queueing (MLFQ) algorithm to mitigate network performance issues like latency, jitter, and bufferbloat. 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The MLFQ prioritizes packet scheduling based on real-time requirements, ensuring low latency for critical applications like video conferencing, online gaming, and streaming services.\n\n## Overview\nBufferbloat 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.\n\n## Features\n- **Priority-based scheduling**: Categorizes packets into different priority queues.\n- **Real-time adaptation**: Uses a feedback mechanism to promote long-waiting packets.\n- **Latency Reduction**: Minimizes delay for real-time applications.\n- **Efficient Bandwidth Utilization**: Ensures fair distribution of bandwidth.\n- **Simulation of Network Traffic**: Models packet scheduling in a simulated environment.\n\n## Usage\nModify 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.\n\n## Algorithm Details\n### 1. **Packet Classification**\n   - Video conferencing packets (UDP, ports 16384-32767) → **Highest Priority**\n   - Online gaming packets (UDP, ports 3478-3480) → **High Priority**\n   - Streaming packets (TCP, keyword 'video') → **Medium Priority**\n   - File downloads (Other TCP/UDP traffic) → **Lowest Priority**\n\n### 2. **MLFQ Structure**\n   - **Level 0:** Real-time critical packets (video calls, gaming)\n   - **Level 1:** Streaming packets\n   - **Level N:** Non-time-sensitive traffic (downloads, background processes)\n\n### 3. **Feedback Mechanism**\n   - If a packet waits too long in a lower-priority queue, it gets promoted to a higher-priority queue to prevent starvation.\n\n## Results\nThe simulation evaluates MLFQ efficiency using the following metrics:\n- **Packet processing time**\n- **Queue wait times**\n- **Dropped packets due to congestion**\n- **Improved latency for real-time applications**\n\n## Future Work\n- Implement **machine learning** for dynamic traffic classification.\n- Optimize **aging mechanisms** to balance fairness and efficiency.\n- Extend support for **Software-Defined Networking (SDN)** environments.\n\n## References\nRelevant research papers and sources used in this project:\n- J. Gettys and K. Nichols, \"Bufferbloat: Dark buffers in the Internet,\" Communications of the ACM, 2012.\n- K. Nichols and V. Jacobson, \"Controlling queue delay,\" ACM, 2012.\n- Various studies on SFQ, CoDel, and FQ-CoDel techniques.\n\n---\n### Author\nDeveloped by **Balkishan**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkishan-25%2Fsolving-bufferbloat-using-multilevel-feedback-queues-mlfq-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkishan-25%2Fsolving-bufferbloat-using-multilevel-feedback-queues-mlfq-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkishan-25%2Fsolving-bufferbloat-using-multilevel-feedback-queues-mlfq-/lists"}