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Improving RED algorithm congestion control by using the Markov decision process.

Amar A Mahawish1,2, Hassan J Hassan3

  • 1Computer Engineering Department, University of Technology-Iraq, Baghdad, Iraq. ce.19.17@grad.uotechnology.edu.iq.

Scientific Reports
|August 3, 2022
PubMed
Summary
This summary is machine-generated.

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A new Markov decision process RED (MDPRED) algorithm enhances internet congestion control by adapting queue weights during TCP Slow Startup. This method improves packet throughput and reduces packet drops compared to traditional algorithms.

Area of Science:

  • Computer Science
  • Network Engineering
  • Algorithm Optimization

Background:

  • Internet congestion control is vital for data transmission performance.
  • Active Queue Management (AQM) algorithms like Random Early Detection (RED) address limitations of TCP's drop-tail mechanism.
  • Traditional RED requires manual parameter tuning, while Adaptive RED (ARED) offers automatic adjustments.

Purpose of the Study:

  • To introduce a novel algorithm, Markov decision process RED (MDPRED), for enhanced internet congestion control.
  • To adapt RED algorithm's queue weight parameter dynamically using Markov decision process (MDP) based on average queue length.
  • To improve TCP performance, particularly during the Slow Start phase, by optimizing congestion management.

Main Methods:

  • Developed and simulated the MDPRED algorithm using the open-source network simulator NS3.

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  • Analyzed the algorithm's performance based on fluctuations in service rate, queuing weight, and mean queue length.
  • Evaluated MDPRED against five other algorithms, focusing on end-to-end throughput and congestion response.

    • Main Results:

    • MDPRED demonstrated efficient end-to-end packet throughput and a rapid response to network congestion.
    • The algorithm achieved a significantly lower level of packet drops compared to benchmark algorithms.
    • Performance improvements were noted as the average queue size approached the maximum queue length threshold.

    Conclusions:

    • The proposed MDPRED algorithm effectively enhances internet congestion control by dynamically adjusting queue weights.
    • MDPRED offers superior performance over traditional RED and other algorithms, especially in reducing packet loss.
    • The study validates the efficacy of using MDP for optimizing AQM parameters in network traffic management.