1 / 24

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

base
Download Presentation

Diffusion Early Marking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related