4.6 Article

New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions

Journal

APPLIED SCIENCES-BASEL
Volume 12, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/app122111176

Keywords

congestion control; active queue management; random early detection; beta distribution; stability

Funding

  1. Agencia Estatal de Investigacion, Spain [PID2019-108654GB-I00/AEI/10.13039/501100011033]
  2. Generalitat Valenciana, Spain [PROMETEO/2021/063]

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In this paper, a simple and robust RED-type algorithm, along with its dynamic variants, is presented to improve the performance of TCP/IP networks. These algorithms can adapt to different network environments and user needs, maintaining a stable queue length and adjusting dynamically according to changing network traffic conditions.
In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In this paper, we present a simple and robust RED-type algorithm together with a couple of dynamical variants with the ability to adapt to the specific characteristics of different network environments, as well as to the user's needs. We first present a basic version called Beta RED (BetaRED), where the parameters can be tuned according to the specific network conditions. The aim is to introduce control parameters that are easy to interpret and provide a good performance over a wide range of values. Secondly, BetaRED is used as a framework to design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED) and Dynamic Beta RED (DBetaRED). In those new algorithms, certain parameters are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. Finally, we present a battery of simulations using the Network Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how to adjust the parameters of the BetaRED mechanism, and to show a performance comparison of ABetaRED and DBetaRED with other representative algorithms that pursue a similar objective.

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