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RMHIL: A Rule Matching Algorithm Based on Heterogeneous Integrated Learning in Software Defined Network.

Yiping Guo1, Guyu Hu1, Dongsheng Shao2

  • 1Command and Control Engineering College, People's Liberation Army Engineering University, Nanjing 210007, China.

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Summary
This summary is machine-generated.

This study introduces a new machine learning algorithm for software-defined networking (SDN) rule matching. The proposed method significantly reduces matching time and memory overhead in large-scale networks.

Keywords:
SDNflow forwardingheterogeneous integrated learningrule matching

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Area of Science:

  • Computer Science
  • Network Engineering
  • Machine Learning

Background:

  • Efficient operation of large-scale networks relies on optimized flow scheduling in Software-Defined Networking (SDN).
  • Rule matching in SDN demands minimal time and memory overhead for efficient network management.

Purpose of the Study:

  • To address the challenge of low rule matching time and memory overhead in SDN.
  • To develop an effective algorithm for SDN flow forwarding rule matching.

Main Methods:

  • Integrating machine learning techniques: recurrent neural networks, reinforcement learning, and decision trees.
  • Transforming the SDN rule matching problem into a heterogeneous integrated learning problem.
  • Designing and implementing a novel algorithm, RMHIL (Rule Matching using Heterogeneous Integrated Learning).

Main Results:

  • RMHIL demonstrates significant advantages in reducing matching time compared to existing algorithms.
  • The proposed RMHIL algorithm also shows a reduction in memory overhead.
  • Comparative experiments validate the performance benefits of RMHIL.

Conclusions:

  • RMHIL offers an effective solution for optimizing SDN rule matching.
  • The integration of heterogeneous machine learning methods enhances SDN performance.
  • This approach contributes to more efficient large-scale network operations.