Rafael C. Nunez - Gonzalo R. Arce Department of Electrical and Computer Engineering University of Delaware May 19 th, 2005 Diffusion Marking Mechanisms for Active Queue Management

2 TCP Congestion Control TCP controls congestion at end points (AIMD)

3 Dropping Packets in the Router’s Queue Tail Dropping Problems:  Penalizes bursty traffic  Discriminates against large propagation delay connections.  Global synchronization. Solution: Active Queue Management (AQM).

4 Active Queue Management Router becomes active in congestion control. Random Early Detection (Floyd and Jacobson, 1993). RED Drop Tail (Not AQM)

5 Random Early Detection (RED) Drop probability based on average queue: Four parameters:  q min, q max, P max, w q  Overparameterized ECN marking

6 Queue Behavior in RED 20 new flows every 20 seconds q min = 20, q max = 40 Wq = 0.01 Wq = 0.001

7 Extensive Research in AQM Adaptive RED, REM, GREEN, BLUE,… Problems:  Over-parameterization  Not easy to implement in routers  Not much better performance than drop tail We introduce a statistical approach

8 Diffusion Marking Mechanisms Three components in AQM algorithms:  Drop Probability Function  Packet Dropping Scheme (Quantizer)  Packet Selection Algorithm (Not exploited yet)

9 Defining a New Packet Dropping Scheme with Error Diffusion Packet marking is analogous to quantization: convert a continuous gray-scale image into black or white dots. Error diffusion: The error between input (continuous) and output (quantized) is diffused in subsequent outputs.

10 Packet Marking in DM D(n) is a quantized representation of P(n) Acumulated Error Feedback model Condition for stability

11 Error Diffusion vs. Random Drops

12 Probability of Marking a Packet Gentle RED function closely follows: (A)

13 Evolution of the Congestion Window TCP in steady state: (B)

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

15 Probability Function

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

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

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

19 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:

20 Dynamic Detection of Active Flows (cont’d) The number of packets: The number of active flows :

21 Active Flows Estimate

22 Results - Window Size RED Diffusion Based Larger congestion window  more data!

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

24 Changing the Number of Flows 20 new flows every 20 seconds RED Diffusion Based

25 Long Lived Flows

26 Long Lived Flows (cont’d)

27 HTTP Flows

28 HTTP Flows (cont’d)

29 Evolution of DM DM has evolved to avoid the estimation of network parameters (RTT, N). The new approach uses a maximum likelihood ratio for congestion detection. Queue Size Dropping Rate

30 Conclusions Error Diffusion dithering can be used in AQM. Advantages:  Increased stability  Simpler (only one parameter)  Increased throughput Current Work:  Parameter optimization  Additional traffic control applications  Extension to wireless environments