Active Queue Management

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Active Queue Management
Rong Pan Cisco System EE384y Spring Quarter 2008
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Outline

• Queue Management
• Drop as a way to feedback to TCP sources
• Part of a closed-loop
• Traditional Queue Management
• Drop Tail
• Problems
• Active Queue Management
• RED
• CHOKe
• AFD

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Queue Management Drops/Marks- A Feedback
Mechanism To Regulate End TCP Hosts

• End hosts send TCP traffic -gt Queue size
• Network elements, switches/routers, generate
  drops/marks based on their queue sizes
• Drops/Marks regulation messages to end hosts
• TCP sources respond to drops/marks by cutting
  down their windows, i.e. sending rate

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TCPQueue Management- A closed-loop control
system
C
W/R
_
q

?
N
?

_
0.5
-
p
Queue Management
Time Delay

1
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Drop Tail- problems

• Lock out
• Full queue
• Bias against bursty traffic
• Global synchronization

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Tail Drop Queue ManagementLock-Out
Max Queue Length
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Tail Drop Queue Management Full-Queue

• Only drop packets when queue is full
• long steady-state delay

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Bias Against Bursty Traffic
Max Queue Length
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Tail Drop Queue ManagementGlobal Synchronization
Max Queue Length
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Alternative Queue Management Schemes

• Drop from front on full queue
• Drop at random on full queue
• both solve the lock-out problem
• both have the full-queues problem

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Active Queue ManagementGoals

• Solve tail-drop problems
• no lock-out behavior
• no full queue
• no bias against bursty flow
• no global synchronization
• Provide better QoS at network nodes
• low steady-state delay
• lower packet dropping

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Random Early Detection (RED)
Arriving packet
AvgQsize gt Minth?
no
yes
Admit the new packet
AvgQsize gt Maxth?
no
yes
end
Drop the new packet
Admit packet with a probability p
end
end
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RED Dropping Curve
1
Drop Probability
maxp
0
minth
maxth
Average Queue Size
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Effectiveness of RED- Lock-Out Global
Synchronization

• Packets are randomly dropped
• Each flow has the same probability of being
  discarded

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Effectiveness of RED- Full-Queue Bias against
bursty traffic

• Drop packets probabilistically in anticipation of
  congestion
• not when queue is full
• Use qavg to decide packet dropping probability
  allow instantaneous bursts

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What QoS does RED Provide?

• Lower buffer delay good interactive service
• qavg is controlled to be small
• Given responsive flows packet dropping is
  reduced
• early congestion indication allows traffic to
  throttle back before congestion
• Given responsive flows fair bandwidth allocation

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Bad News - unresponsive end hosts
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Scheduling Queue Management

• What routers want to do?
• isolate unresponsive flows (e.g. UDP)
• provide Quality of Service to all users
• Two ways to do it
• scheduling algorithms
• e.g. FQ, CSFQ, SFQ
• queue management algorithms with fairness
  enhancement
• e.g. CHOKe, AFD, WRED

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Active Queue Manament With Enhancement to Fairness
FIFO

• Approximate fair bandwidth allocation
• Provide isolation from unresponsive flows
• Be as simple as RED

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RED
Arriving packet
AvgQsize gt Minth?
no
yes
Admit the new packet
AvgQsize gt Maxth?
no
yes
end
Drop the new packet
Admit packet with a probability p
end
end
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Random Sampling from Queue
UDP flow

• A randomly chosen packet more likely from the
  unresponsive flow

• Adversary cant fool the system

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Comparison of Flow ID

• Compare the flow id with the incoming packet
• more acurate
• Reduce the chance of dropping packets from a
  TCP-friendly flows.

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Dropping Mechanism

• Drop packets (both incoming and matching samples
  )
• More arrival -gt More Drop
• Give users a disincentive to send more

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Simulation Setup
D(1)
S(1)
10Mbps
S(2)
D(2)
10Mbps
m TCP
m TCP
Sources
Sinks
1Mbps
S(m)
D(m)
S(m1)
D(m1)
n UDP Sources
n UDP Sinks
S(mn)
D(mn)
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Network Setup Parameters

• 32 TCP flows, 1 UDP flow
• All TCPs maximum window size 300
• All links have a propagation delay of 1ms
• FIFO buffer size 300 packets
• All packets sizes 1 KByte
• RED (minth,maxth) (100,200) packets

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32 TCP, 1 UDP (one sample)
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32 TCP, 5 UDP (5 samples)
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How Many Samples to Take?
Maxth
minth
avg

• Different samples for different Qlenavg
• samples ? when Qlenavg close to minth
• samples? when Qlenavg close to maxth

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32 TCP, 5 UDP (self-adjusting)
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Analytical Model
discards from the queue
permeable tube with leakage
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Fluid Analysis

• N the total number of packets in the buffer
• Li(t) the survival rate for flow i packets

Li(t)?t - Li(t ?t)?t ?i ?t Li(t)?t /N -
dLi(t)/dt ?i Li(t) N Li(0) ?i (1-pi
) Li(D) ?i (1-2pi )
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Model vs Simulation- multiple TCPs and one UDP
1/(1e)
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Fluid Model - Multiple samples

• Multiple samples are chosen

Li(t)?t - Li(t ?t)?t M?i ?t Li(t)?t /N -
dLi(t)/dt M?i Li(t) N Li(0) ?i (1-pi
)M Li(D) ?i (1-pi )M - M?i pi
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Two Samples- multiple TCPs and one UDP
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Two Samples- multiple TCPs and two UDP
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What If We Use a Small Amount of State?
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AFD Goal

• Approximate weighted bandwidth allocation
• Not only AQM, approximate WDRR scheduling
• Provide soft queues in addition to physical
  queues
• Keep the state requirement small
• Be simple to implement

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AFD Algorithm Details (Basic Case Equal Share)
Di Drop Probability for Class i
Arriving Packets
Qlen
1-Di
Qref
Class i
Di
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AFD Algorithm Details (General Case)
Di Drop Probability for Class i
Arriving Packets
Qlen
1-Di
Qref
Class i
Di
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Not Per-Flow State
Fraction of flows

• State requirement on the order of of
  unresponsive flows
• Elephant Traps (developed jointly Stanford and
  Cisco)

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AFD Solution Details

• Based on 3 simple mechanisms
• estimate per class arrival rate
• counting per class bytes over fixed intervals
  ( Ts )
• potential averaging over multiple intervals
• estimate deserved departure rate (so as to
  achieve the proper bandwidth allocation for each
  class)
• observation of queue length as measure of
  congestion
• perform selective dropping (pre-enqueue) to drive
  arrival rate to the desired departure rate

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Mixed Trafficwith Different Levels of
Unresponsiveness
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Drop Probabilities(note differential dropping)
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Different Number of TCP Flows in Each Class
10 TCP Flows
Class 2
Class 1
5 TCP Flows
0
50
150
200
100
time
0
50
150
200
100
time
20 TCP Flows
15 TCP Flows
Class 4
Class 3
0
50
150
200
100
time
0
50
150
200
100
time
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Different Class Throughput Comparison
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Queue Length
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Mfair
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AFD Implementation Issues

• Monitor Arrival Rate
• Determine Drop Probability
• Maximize Link Utilization

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Arrival Monitoring

• Keep a counter for each class
• Count the data arrivals (in bytes) of each class
  in 10ms interval arvi
• Arrival rate of each class is updated every 10ms
• mi mi(1-1/2c)arvi
• c determines the average window

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Implementing the Drop Function

• If Mi ? Mfair then Di 0
• Otherwise, rewrite the drop function as
• Suppose we have predetermined drop levels, find
  the one such that Di Mi (Mi Mfair)

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Implementing the Drop Function

• Drop levels are 1/32, 1/16, 3/32

• Suppose mi 100, mfair 62.0 gt Di 0.380,

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AFD - Summary
Fairness
RED
Simplicity

• Equal share is approximated in a wide variety of
  settings
• The state requirement is limited

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Summary

• Traditional Queue Management
• Drop Tail, Drop Front, Drop Random
• Problems lock-out, full queue, global
  synchronization, bias against bursty traffic
• Active Queue Management
• RED cant handle unresponsive flows
• CHOKe penalize unresponsive flows
• AFD provides approximate fairness with limited
  states