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Active Transport01:14

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Active transport is a critical biological process that allows cells to move solutes against an electrochemical gradient. This process requires direct energy input and is characterized by its selectivity, saturability, and susceptibility to competitive inhibition.
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Adaptive Hurst-Sensitive Active Queue Management.

Dariusz Marek1, Jakub Szyguła1, Adam Domański1

  • 1Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Entropy (Basel, Switzerland)
|March 25, 2022
PubMed
Summary
This summary is machine-generated.

We introduce adaptive AQM mechanisms that use traffic self-similarity for better network performance. This approach reduces queue occupancy, packet loss, and transmission latency in network traffic management.

Keywords:
PIDadaptive AQMneural networksreinforcement learningself similarity

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Communications

Background:

  • Active Queue Management (AQM) mechanisms, like Random Early Detection (RED), enhance network transmission efficiency but depend critically on parameter selection.
  • Adaptive AQM mechanisms adjust parameters based on network conditions, such as traffic intensity, exemplified by Adaptive RED (ARED).
  • Network traffic often exhibits self-similar characteristics, which are not fully exploited by existing adaptive AQM algorithms.

Purpose of the Study:

  • To propose novel adaptive AQM mechanisms that incorporate the degree of traffic self-similarity (Hurst parameter) for improved parameter tuning.
  • To modify existing AQM algorithms (ARED, fractional order PIαDβ) and neural network-based algorithms to utilize both traffic intensity and self-similarity.
  • To evaluate the performance of these enhanced AQM mechanisms in reducing network congestion metrics.

Main Methods:

  • Development of modified ARED, fractional order PIαDβ, and neural network-based AQM algorithms incorporating the Hurst parameter.
  • Utilizing Fluid Flow approximation and discrete event simulation for performance evaluation.
  • Analysis of average queue occupancies and packet delays under various network conditions.

Main Results:

  • The proposed adaptive AQM mechanisms, considering traffic self-similarity, significantly decrease average queue occupancy compared to basic counterparts.
  • A reduction in the number of rejected packets was observed when using the enhanced AQM algorithms.
  • The study demonstrated a notable decrease in transmission latency due to the incorporation of self-similarity in AQM parameter determination.

Conclusions:

  • Incorporating the degree of network traffic self-similarity into AQM parameter adjustment offers substantial performance improvements.
  • The modified AQM algorithms provide a more robust and efficient approach to managing network queues and reducing congestion.
  • This research highlights the importance of characterizing traffic self-similarity for advanced network performance optimization.