Active Queue Management: Theory, Experiment and Implementation Vishal Misra Dept. of Computer Science Columbia University in the City of New York

Collaborators C.V. Hollot, Don Towsley: UMass Amherst Victor Firoiu: Nortel Networks Kevin Jeffay, Nguyen-Long Le, Don Smith: UNC Chapel Hill

Outline Investigating rate based control Implementation of PI controller –Hardware –Software Experiment –Performance evaluation under generated web traffic

TCP dynamic queue dynamic AQM p W q time delay R secs MGT Fluid-Flow Model “oscillatory behavior increases with increasing round- trip time”

TCP dynamic AQM p W time delay R secs Kelly “oscillatory behavior decreases with increasing round- trip time” x

Paradox? MGT model : control based on queue length (q) Kelly model : control based on arrival rate (x) Rate Feedback p = g(x) =

Utilization with different B

Rate Feedback p = g(x) =  p  W time delay R secs  x -  p(t - R) linearization

L(s)L(s)  p -  p(t - R) where W 0 satisfies: rate feedback loop (*)

unstable for  > 0.3 Stability (B=1) Stability  distance of Nyquist plot from –1+j0 N=60 flows C=3750 packets/sec

Simulations at RTT =300 ms unstable for  > 0.3 N=60 flows C=3750 packets/sec

Parabolic rate feedback B = 2 Where, W 0 satisfies:

Multiple Equilibria (Throughput)

Multiple Equilibria (Stability)

Stability (B=2) unstable for  > 0.8 N=60 flows C=3750 packets/sec

Simulations at RTT = 300 ms N=60 flows C=3750 packets/sec

Implementation and Experiments

Implementing PI controller PI q(t) p(t) q ref Integral controller, regulates router buffer to some operator controlled value q ref

Hardware Implementation Active collaboration with two vendors on implementing PI on a router –Nortel Networks: Next generation edge router –Cisco: IOS on the 3260 platform

Transitioning from theory to practice (Nortel) Theory, Simulations: Worry about computations at one output queue, for a single class of traffic Practice: Typical router has M (~ 512) queues, E (~ 8) classes

Speed Issues Consider a 10 GBps router, 1000 byte average packet size Theory: Sampling interval (say) 1 ms: computational overhead spread over packets: “lightweight computations” Practice: Sampling interval 1ms, MxE (512x8) computations: spread over 10 packets: significant overhead!

Memory issues Theory: One drop/marking probability needs to be maintained Practice: MxE values have to be maintained! Hardware designers unwilling to allot memory real estate for AQM (relatively small part of a router) Solution: Discretize [0,1] and use small precomputed tables

Architecture 0101 Append pointer to probability lookup table Table for class i packet Packet from priority class i Lookup probability Dropping module Small (~8) number of tables used with finite (~ 16) entries

Open research issues How do you discretize [0,1] ? –Linear is clearly not the answer: operating region typical below 0.2 Given a typical operating range of p : what performance metric do we optimize? What is the cost function?

Software Implementation of PI “Tuning RED for Web Traffic”, Sigcomm 2000 –Implemented RED on a software router (the ALTQ system running on FREEBSD) –Compared performance of RED and FIFO (Droptail) on a testbed with generated Web traffic: studied request completion latency –Conclusions: RED normally does not help, difficult to tune for scenarios when it can help (read: “RED only possibly helps in really extreme cases and even here it's hard as hell to get the settings right”) AQM bad idea? Study of AQM at UNC

Handwaving explanation FIFO RED More losses, more retransmissions, more timeouts..-> higher latency!

UNC Testbed

PI Implementation on ALTQ PI added as a module to ALTQ at UNC Issues: no floating point arithmetic allowed, need to be careful about saturation, integer overflows! Sigcomm 2000 experiments repeated under (nearly) identical conditions with PI as third mechanism PI tuned using formula given in Infocom 2000 paper

Plot of CDF of response time of requests (80% load) Cumulative probability Response time (ms)

Plot of CDF of response time of requests (100% load) Cumulative probability Response time (ms) PI, qref=20 FIFO, RED PI, qref=200

Plot of CDF of response time of requests (110% load) Cumulative probability Response time (ms) PI, qref=20 PI, qref=200 FIFO, RED

Preliminary conclusions AQM may not be bad after all: PI/20 performs significantly better for short objects under heavy load Experiments run with packet dropping, not ECN ECN experiments planned: performance should improve dramatically over FIFO