Diffusion Mechanisms for Active Queue Management Department of Electrical and Computer Engineering University of Delaware Aug 19th / 2004 Rafael Nunez Gonzalo Arce

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

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

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

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 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 Queue Behavior in RED

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

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 REM vs. RED

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

13 Digital Halftoning Original Image Ordered Dither Error Diffusion

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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 AQM Dynamics with nonlinearity

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.

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33 AQM Dynamics with nonlinearity (2)

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

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

37 Effects of Threshold Modulation in the Control Mechanism

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

40 Active Flows Estimate

41 Diffusion Mechanisms for Active Queue Management RESULTS

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

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

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

45 Long lived flows

46 Long lived flows (2)

47 Long lived flows (3)

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 Http flows

50 Http flows (2)

51 Http flows (3)

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

Thank you! Department of Electrical and Computer Engineering University of Delaware Aug 19th / 2004 Rafael Nunez Gonzalo Arce