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Platelet Adhesion and Aggregation Under Flow using Microfluidic Flow Cells
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Entry Aggregation and Early Match Using Hidden Markov Model of Flow Table in SDN.

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Summary

This study introduces Agg-ExTable to manage multiple flow tables (MFT) in software-defined networking (SDN). The novel approach reduces MFT space by 45% and processing time using flow entry aggregation and a front-end ExTable.

Keywords:
Hidden Markov ModelQuine–McCluskey AlgorithmSDNentry aggregationmatch frequency and probability

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Area of Science:

  • Computer Science
  • Networking

Background:

  • Software-Defined Networking (SDN) utilizes multiple flow tables (MFT) for enhanced flexibility.
  • MFT size is constrained by expensive Ternary Content Addressable Memory (TCAM), and pipeline mechanisms lead to long flow processing times.

Purpose of the Study:

  • To propose a novel approach, Agg-ExTable, for efficient management of MFT in SDN.
  • To address the limitations of MFT size and flow processing time.

Main Methods:

  • Flow entries in MFT are periodically aggregated using pruning and the Quine-McCluskey algorithm.
  • A front-end ExTable is created using the saved memory to store popular flow entries for early matching.
  • Popular entries are identified using a Hidden Markov model based on match frequency and probability.

Main Results:

  • The Agg-ExTable scheme achieves approximately 45% space saving for MFT.
  • The proposed scheme significantly decreases flow processing time compared to existing methods.

Conclusions:

  • Agg-ExTable offers an efficient solution for managing MFT in SDN environments.
  • The approach effectively overcomes TCAM limitations and reduces processing latency.