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Diffusion Early Marking

Diffusion Early Marking. Gonzalo Arce arce@ece.udel.edu. Rafael Nunez nunez@ece.udel.edu. Department of Electrical and Computer Engineering University of Delaware May / 2004. Diffusion Early Marking. Introduction Diffusion Early Marking Model Optimizations. Parameters Estimation

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Diffusion Early Marking

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  1. Diffusion Early Marking Gonzalo Arce arce@ece.udel.edu Rafael Nunez nunez@ece.udel.edu Department of Electrical and Computer Engineering University of Delaware May / 2004

  2. Diffusion Early Marking • Introduction • Diffusion Early Marking • Model Optimizations. • Parameters Estimation • Performance • Conclusions and Future Work

  3. The Internet Today

  4. Desirable control: distributed, simple, stable and fair. Congestion

  5. Problems with Tail Dropping • Penalizes bursty traffic • Discriminates against large propagation delay connections. • Global synchronization.

  6. Active Queue Management (AQM) • Random Early Detection (Floyd and Jacobson, 1993) • Router becomes active in congestion control. • RED has been deployed in some Cisco routers.

  7. Random Early Detection (RED) • Random packet drops in queue. • Drop probability based on average queue: • Four parameters: • qmin • qmax • Pmax • wq (overparameterized)

  8. Queue Behavior in RED

  9. Queue Behavior in RED (2) • 20 new flows every 20 seconds • Wq = 0.01 • Wq = 0.001

  10. Adaptive RED, REM, GREEN, BLUE,… Problems: Over-parameterization Not easy to implement in routers Not much better performance than drop tail Other AQM’s Schemes

  11. REM vs. RED

  12. Diffusion Mechanisms for AQM • Instantaneous queue size. • Better packet marking strategy. • Simplified parameters.

  13. Error Diffusion • Packet marking is analogous to halftoning: • Convert a continuous gray-scale image into black or white dots • Packet marking reduces to quantization • Error diffusion: The error between input (continuous) and output (discrete) is incorporated in subsequent outputs. • P[n] is the drop probability

  14. Where: Diffusion Mechanism

  15. Probability of Marking a Packet • Gentle RED function closely follows: (A)

  16. Evolution of the Congestion Window • TCP in steady state: (B)

  17. Traffic in the Network Congestion Window = Packets In The Pipe + Packets In The Queue Or: (C) • From (A), (B), (C), and knowing that: where

  18. Probability Function

  19. Significant Flows • If number of flows exceeds capacity, then some of the flows timeout • 0 flows in timeout  Ef = 1 • Some flows in timeout  Ef = (0.8 ~ 1) • Most of the flows in timeout.  Efa1/N

  20. Algorithm Summary • Diffusion Early Marking decides whether to mark a packet or not as: Where: Remember: M=2, b1=2/3, b2=1/3

  21. Number of Flows • The number of significant flows:

  22. Stability of the Queue • 100 long lived connections (TCP/Reno, FTP) • Desired queue size = 30 packets

  23. 20 new flows every 20 seconds Changing the number of flows

  24. Conclusions and Future Work • Queue length stabilized and controlled without adjusting parameters. • Diffusion mechanism improves the behavior of the proposed AQM scheme. • Future Work: • Optimize the estimation of parameters • Analyze more traffic scenarios • Complete the performance measures: fairness, throughput • Compare with other AQMs • Use diffusion mechanism in other AQMs

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