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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.

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active queue management theory experiment and implementation

Active Queue Management: Theory, Experiment and Implementation

Vishal Misra

Dept. of Computer Science

Columbia University in the City of New York

collaborators
Collaborators
  • C.V. Hollot, Don Towsley: UMass Amherst
  • Victor Firoiu: Nortel Networks
  • Kevin Jeffay, Nguyen-Long Le, Don Smith: UNC Chapel Hill
outline
Outline
  • Investigating rate based control
  • Implementation of PI controller
    • Hardware
    • Software
  • Experiment
    • Performance evaluation under generated web traffic
slide4

MGT Fluid-Flow Model

q

W

TCP

dynamic

queue

dynamic

p

time delay

R secs

AQM

“oscillatory behavior increases with increasing round-trip time”

slide5

Kelly

W

x

TCP

dynamic

p

time delay

R secs

AQM

“oscillatory behavior decreases with increasing round-trip time”

paradox
Paradox?
  • MGT model : control based on queue length (q)
  • Kelly model : control based on arrival rate (x)

Rate Feedback p = g(x) =

slide8

Rate Feedback p = g(x) =

linearization

d x

d W

-

d p(t - R)

d p

time delay

R secs

slide9

L(s)

-

rate feedback loop

d p

dp(t - R)

where W0 satisfies:

(*)

slide10

Stability (B=1)

N=60 flows

C=3750 packets/sec

unstable for  > 0.3

Stability  distance of Nyquist plot from –1+j0

slide11

unstable for  > 0.3

Simulations at RTT =300 ms

N=60 flows

C=3750 packets/sec

slide12

Parabolic rate feedback B = 2

Where, W0 satisfies:

slide15

Stability (B=2)

N=60 flows

C=3750 packets/sec

unstable for  > 0.8

slide16

Simulations at RTT = 300 ms

N=60 flows

C=3750 packets/sec

implementing pi controller
Implementing PI controller

PI

p(t)

q(t)

qref

Integral controller, regulates router buffer to some

operator controlled value qref

hardware implementation
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
Transitioning from theory to practice (Nortel)
  • Theory, Simulations: Worry about computations at one output queue, for a singleclass of traffic
  • Practice: Typical router has M(~ 512) queues, E (~ 8) classes
speed issues
Speed Issues
  • Consider a 10 GBps router, 1000 byte average packet size
  • Theory: Sampling interval (say) 1 ms: computational overhead spread over 40000 packets: “lightweight computations”
  • Practice: Sampling interval 1ms, MxE (512x8) computations: spread over 10 packets: significant overhead!
memory issues
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

Table for class i

Dropping

module

Packet from priority class i

0101

.12767

packet

0101

Lookup probability

Append pointer to probability lookup table

Architecture

Small (~8) number of tables used with finite (~ 16) entries

open research issues
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

Study of AQM at UNC

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 Webtraffic: 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?

handwaving explanation

RED

Handwaving explanation

FIFO

More losses, more retransmissions, more timeouts..-> higher latency!

pi implementation on altq
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
slide29

Plot of CDF of response time of requests (80% load)

Cumulative probability

Response time (ms)

slide30

Plot of CDF of response time of requests (100% load)

FIFO, RED

PI, qref=20

Cumulative probability

PI, qref=200

Response time (ms)

plot of cdf of response time of requests 110 load
Plot of CDF of response time of requests (110% load)

FIFO, RED

PI, qref=20

Cumulative probability

PI, qref=200

Response time (ms)

preliminary conclusions
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