Transcript A Comparison of Load-based and Queue-based Active Queue Management Algorithms Minseok Kwon and Sonia Fahmy Department of Computer Sciences Purdue University {kwonm, fahmy}@cs.purdue.edu All our slides and.

A Comparison of
Load-based and Queue-based
Active Queue Management Algorithms
Minseok Kwon and Sonia Fahmy
Department of Computer Sciences
Purdue University
{kwonm, fahmy}@cs.purdue.edu
All our slides and papers are available at:
http://www.cs.purdue.edu/~fahmy/
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Congestion Control
Active Queue Management (AQM)
TCP Congestion Control
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TCP Congestion Control
Slow-Start
Congestion Avoidance
cwnd
Additive Increase
Multiplicative Decrease (AIMD)
ssthresh
1
TCP-Reno
3 DupAck
Timeout
time
new ssthresh = cwnd / 2
3
Why Active Queue
Management?
• Controls average queue size.
• Absorbs bursts without dropping
packets.
• Prevents bias against bursty
connections.
• Avoids global synchronization of TCP.
• Reduces the number of timeouts in TCP.
• Punishes misbehaving flows.
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Queue-based
Active Queue Management
• RED [Floyd and Jacobson, 1993]
• Drops packets probabilistically in proportion to a long term
average queue length (buffer occupancy).
• SRED [Ott et al., 1999]
• The packet loss probability is proportional to the
instantaneous buffer occupancy and the estimated number
of active flows.
• FRED [Lin and Morris, 1997]
• Imposes on each flow a loss probability proportional to the
flow average and instantaneous buffer occupancy.
• BLUE [Feng et al., 2001]
• Increments the packet drop probability when packet loss
occurs and decrements the packet drop probability if the link
is idle.
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Load-based
Active Queue Management
• REM [Athuraliya et al., 1999]
• Congestion measure (price) is computed proportionally to the
difference between input rate and output rate and current buffer
occupancy at router. Source rate is computed inversely
proportional to the congestion measure. Thus, the source
reaches a globally optimal equilibrium.
• AVQ [Kunniyur and Srikant, 2001]
• Maintains a virtual queue. When the virtual queue overflows,
packets in a real queue are marked/dropped. The virtual
capacity is modified such that total flows achieve a desired
utilization of the link.
• PI controller [Hollot et al., 2001]
• Queue length slope determines packet drop probability and the
queue is regulated to the desired queue length.
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Queue-based AQM: RED
No dropping
or marking
Pdrop/mark
Drop with P=1
1
Pmax
Mark with P
Linearly increasing
From 0 to Pmax
Average Queue Length
0
Thmin
Thmax
Qavg
Drop Probability P
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Drawbacks of Queue-based AQM
• Insensitive to current queue arrival and drain rates.
• Long term queue length average produces slow
response.
• Difficult to configure parameters.
Small drop probability
From small queue
After some period
Large drop probability
After some period
From large queue
Large queue
Small queue
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Objectives
• Is queue length a sufficient congestion
indicator?
• Can we use load information for more precise
congestion indication?
• Is queue length information still important,
even when the load information is used?
• How do RED, SRED, FRED, BLUE, and REM
compare in terms of user-perceivable metrics,
such as Web response time?
• Can we achieve both high responsiveness and
high throughput?
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Load/Delay Control (LDC)
• Load factor
• R = queue arrival rate / queue service rate
• Provides better load (input/output rate)
response and more intuitive
parameters.
• Maintains RED benefits: misbehaving
flow punishment and global
synchronization avoidance.
• Unlike many load-based schemes, ECN
is not required.
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Load/Delay Control (LDC)
• Uses both queue length information and
load factor as multiple time scale
congestion indicators.
• Long-term: Queue length gives a more
stable (slower) congestion indication.
• Short-term: Load factor enables faster
response.
• Goal: Maintain queuing delay below a
target value and queue arrival rate
below queue service rate.
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Load/Delay Control (LDC)
• Packet drop probability of LDC
Pdrop
3
Qavg n
1
Ravg n
 min(1, (
) )  min(1, (
) )
4
Qtarget
4
Rtarget
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Performance Evaluation
• Network simulator ns-2.1b6
• GFC-2 Configuration
• 22 HTTP on each sender-receiver pair, 1 unlimited
FTP (H), 12 kbps CBR-UDP (F)
• Average 5 simulation runs, each runs 900 seconds
• Performance Metrics
• Web response time (seconds), Goodput (Mbps),
Packet Drop ratio (%), Delay for UDP connections
(seconds)
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Simulation Parameters
• RED
• Thmin=buffer/12, Thmax=buffer/4, w_q=0.002,
max_p=0.1, gentle RED
• SRED
• Full SRED, max_p=0.15, M=1000, p=0.25
• FRED
• Min_q=4, others same as RED
• BLUE
• Decrement =0.02, increment=0.2, freeze_time=0.1 s
• REM
• =0.1, =0.005, =1.001, w_q=0.002, =0.002 s
• LDC
• D_target=4.0 s, (w_q=0.002, w_r=0.998, n=3,
R_target=0.95, =0.002 s)
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Simulation Results
Algorithm
Web
mean
Response
DropTail
79.07
RED
UDP
Pkt
Delay
Web
mean
Goodput
UDP
mean
Goodput
FTP
mean
Goodput
Pkt
Drop
Ratio
0.95 0.110
0.12
0.134
1.31
69.35
0.46 0.098
0.12
0.134
2.86
SRED
74.18
1.30 0.100
0.12
0.129
1.75
FRED
52.86
0.30 0.132
0.11
0.127
4.28
BLUE
79.02
1.40 0.096
0.12
0.132
1.47
REM
62.34
0.40 0.093
0.12
0.125
3.28
LDC
58.85
0.22 0.088
0.12
0.129
4.27
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Merits and Drawbacks of AQM
• RED
• Merits
• Early congestion
detection
• No bias against
bursty traffic
• No global
synchronization
• Drawbacks
• Difficulty in
parameter setting
• Insensitivity to traffic
load and drain rates
• SRED
• Merits
• Stabilized queue
occupancy
• Protection from
misbehaving flows
• Drawbacks
• Some per-flow state
(zombie list)
• RED disadvantages
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Merits and Drawbacks of AQM
• FRED
• Merits
• Good protection from
misbehaving flows
• Drawbacks
• Per-flow state
• RED disadvantages
• BLUE
• Merits
• Simplicity
• High throughput
• Drawbacks
• No early congestion
detection (Pdrop
updated only on
queue overflow or
link idle events)
• Slow response and
dependence on
history
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Merits and Drawbacks of AQM
• REM
• Merits
• Low delay and small
queues
• Independence of the
number of users
• Drawbacks
• Some complexity
due to parameters
• Low throughput for
Web traffic
• Inconsistency with
TCP sender
mechanism; works
best with ECN
• LDC
• Merits
• Sensitivity to traffic
load and drain rate
• Low delay
• Target delay achieved
• Intuitive parameters,
meaningful to users
(target delay)
• Drawbacks
• Some complexity due
to parameters
• Low throughput in
some cases
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Conclusions and Future Work
• Queue-based algorithms are difficult to
configure.
• Load-based algorithms reduce delay, but
sometimes exhibit low throughput.
• A configurable combination that takes into
consideration input rate, drain rate and
queuing delay works well.
• Need to study LDC with ECN, parameter
values of LDC, and the effect of variable
output rate in multiple queues.
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