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AQM controller design for networks supporting TCP vegas: A control theoretical approach

Bigdeli, N ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1016/j.isatra.2007.05.001
  3. Publisher: ISA - Instrumentation, Systems, and Automation Society , 2008
  4. Abstract:
  5. In this paper, a mathematical model and control theoretical framework for designing AQM controllers in networks supporting TCP Vegas is introduced. We have emphasized on a modified TCP Vegas algorithm that can respond to congestion signals through explicit congestion notification (ECN). The overall nonlinear delayed differential equations of the dynamics model of closed loop system have been derived based on TCP Vegas model. The model is then linearized to derive a transfer function representation between the packet marking probability and the bottleneck router queue length as the input and output of the modified TCP Vegas/AQM system. The model properties have been then examined especially for the case of single bottleneck homogeneous network which is closely investigated. Finally an AQM controller based on Coefficient Diagram Method (CDM) has been designed for the system and its performance has been compared with some other AQM controllers. CDM is a new indirect pole placement method that considers the speed, stability and robustness of the closed loop system in terms of time domain specifications. In order for synthesizing the simulation scenarios, our campus router traffic has been studied experimentally for a sample period of one hour and the corresponding parameters has been extracted. The simulation results are representative of good performance of developed TCP Vegas/AQM structure for different simulated scenarios. © 2007 ISA
  6. Keywords:
  7. Congestion control (communication) ; Differential equations ; Internet ; Linearization ; Active queue management ; Coefficient diagram methods ; Network modeling ; TCP Vegas ; Network protocols ; Algorithm ; Artificial neural network ; Statistical model ; Statistics ; Algorithms ; Models, Statistical ; Neural Networks (Computer) ; Quality Control
  8. Source: ISA Transactions ; Volume 47, Issue 1 , 2008 , Pages 143-155 ; 00190578 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/5262685