1. Diffusion Mechanisms for Active Queue Management Department of Electrical and Computer Engineering University of Delaware Aug 19th / 2004 Rafael Nunez nunez@ece.udel.edu Gonzalo Arce arce@ece.udel.edu

2. 2 Diffusion Mechanisms for Active Queue Management Image Processing Approaches to AQM: There is an intimate link between printing technologies and Active Queue Management.

3. 3 The Internet Today TCP: de facto congestion control protocol. 90% of Internet traffic.

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

5. 5 Simplest Congestion Control: Tail Dropping Problems with tail dropping:  Penalizes bursty traffic  Discriminates against large propagation delay connections.  Global synchronization.

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

7. 7 Random Early Detection (RED)  Random packet drops in queue.  Drop probability based on average queue:  Four parameters: q min q max P max w q (overparameterized)

8. 8 Queue Behavior in RED

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

10. 10 How to overcome these problems…  Adaptive RED, REM, GREEN, BLUE,…  Problems: Over-parameterization Not easy to implement in routers Not much better performance than drop tail

11. 11 REM vs. RED

12. 12 Diffusion Mechanisms: Exploiting Image Processing  Our solution  Based on digital halftoning  Halftoning is a successful printing technique: from newspapers to laser printers

13. 13 Digital Halftoning Original Image Ordered Dither Error Diffusion

14. 14

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

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

17. 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. 18 Probability Function

19. 19 AQM Dynamics with nonlinearity

20. 20 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.

21. 21 Diffusion Mechanism ≥

22. 22 Diffusion Mechanism ≥

23. 23 Diffusion Mechanism ≥

24. 24 Diffusion Mechanism ≥

25. 25 Diffusion Mechanism ≥

26. 26 Diffusion Mechanism ≥

27. 27 Diffusion Mechanism ≥

28. 28 Diffusion Mechanism ≥

29. 29 Diffusion Mechanism ≥

30. 30 Diffusion Mechanism ≥

31. 31 Diffusion Mechanism ≥

32. 32 Diffusion Mechanism ≥

33. 33 AQM Dynamics with nonlinearity (2)

34. 34 Algorithm Summary Diffusion Early Marking decides whether to mark a packet or not as: Where: M=2, b 1 =2/3, b 2 =1/3 Remember:

35. 35 Optimizing the Control Mechanism  Adaptive Threshold Control  Dynamic Detection of Active Flows

36. 36 Adaptive Threshold Control  Dynamic changes to the threshold improve the quality of the output.

37. 37 Effects of Threshold Modulation in the Control Mechanism

38. 38 Dynamic Detection of Active Flows  DEM requires the number of active flows  Effect of not-timed out flows and flows in timeout during less than RTT:

39. 39 Dynamic Detection of Active Flows (2)  The number of packets:  The number of active flows:

40. 40 Active Flows Estimate

41. 41 Diffusion Mechanisms for Active Queue Management RESULTS

42. 42 Window Size RED Diffusion Based Larger congestion window  more data!

43. 43 Stability of the Queue  100 long lived connections (TCP/Reno, FTP)  Desired queue size = 30 packets RED Diffusion Based

44. 44 Changing the number of flows  20 new flows every 20 seconds RED Diffusion Based

45. 45 Long lived flows

46. 46 Long lived flows (2)

47. 47 Long lived flows (3)

48. 48 Http flows - model  PackMime traffic model  Internet Traffic Research group at Bell Labs  Traffic controlled by the rate parameter (the average number of new connections started each second)

49. 49 Http flows

50. 50 Http flows (2)

51. 51 Http flows (3)

52. 52 Conclusions  Digital halftoning is a mature technique that can be used in AQM.  Advantages: Increased stability Simpler (only one parameter) Increased throughput  Current Work: Parameter optimization Complete benchmarking Additional traffic control applications

53. Thank you! Department of Electrical and Computer Engineering University of Delaware Aug 19th / 2004 Rafael Nunez nunez@ece.udel.edu Gonzalo Arce arce@ece.udel.edu