Inferring Queue State by Measuring Delay in a
WiFi Network
David Malone, Douglas J Leith, Ian Dangerfield
11 May 2009
Wifi, Delay, Queue Length and Congestion Control
• RTT a proxy for queue length.
• Not too crazy in wired networks.
• For Wifi?
Counting Down
(20us)
Someone else transmits,
stop counting!
Transmissions (~500us)
Data and then ACK
Collision followed by timeout
• With fixed traffic, what is impact of random service?
• What is impact of variable traffic (not even sharing buffer)?
• What will Vegas do in practice?
• Want to understand these for future design work.
Sample Previous work
• V. Jacobson. pathchar — a tool to infer characteristics of
internet paths. MSRI, April 1997.
• N Sundaram, WS Conner, and A Rangarajan. Estimation of
bandwidth in bridged home networks. WiNMee, 2007.
• M Franceschinis, M Mellia, M Meo, and M Munafo.
Measuring TCP over WiFi: A real case. WiNMee, April 2005.
• G. McCullagh. Exploring delay-based tcp congestion control.
Masters Thesis, 2008.
Fixed Traffic: How bad is the problem?
200000
Observed Drain Time
180000
160000
Drain Time (us)
140000
120000
100000
80000
60000
40000
20000
0
0
5
10
Queue Length (packets)
15
20
What do the stats look like?
200000
1400
Drain Time Distribution
Mean Drain Time
Number of Observations
180000
1200
160000
Drain Time (us)
120000
800
100000
600
80000
60000
400
40000
200
20000
0
0
0
5
10
Packets in queue
Note: the variance is getting bigger.
15
20
Number of Observations
1000
140000
How does it grow?
20000
20000
18000
16000
16000
14000
14000
12000
12000
10000
10000
8000
8000
6000
6000
4000
4000
2000
Number of Observations
Drain Time Standard Deviation (us)
Measured Estimate
V sqrt(n)
18000
2000
0
2
4
6
8
10
12
14
Queue Length (packets)
16
18
20
22
For fixed traffic service time looks uncorrelated.
Queue Length Prediction
• Suppose traffic is fixed.
• We have collect all statistics.
• Given an RTT, can we guess how full queue is?
• Easier: more or less than half full?
Results of thresholding
0.6
Delay threshold wrong
Delay Threshold wrong by 50% or more
0.5
Fraction of Packets
0.4
0.3
0.2
0.1
0
0
20000
40000
60000
80000 100000 120000
Threshold (us)
140000
160000
180000
200000
Using History
• Mistake 10% of time, not good for congestion control.
• Only using one sample, what happens if we use history.
• Obvious thing to do: filter.
Filters
7/8 Filter
srtt ← 7/8srtt + 1/8rtt.
Exponential Time
srtt ← e −∆T /Tc srtt + (1 − e −∆T /Tc )rtt.
Windowed Mean
srtt ← mean rtt.
last RTT
Windowed Min
srtt ← min rtt.
last RTT
How much better do we do?
0.6
Exp Time Threshold wrong
Window Mean Threshold wrong
7/8 Filter Threshold wrong
Window Min Threshold wrong
Raw Threshold wrong
Exp Time Threshold wrong by >= 50%
Window Mean Threshold wrong by >= 50%
7/8 Filter Threshold wrong by >= 50%
Window Min Threshold wrong by >= 50%
Raw Threshold wrong by >= 50%
0.5
Fraction of Packets
0.4
0.3
0.2
0.1
0
0
20000
40000
60000
80000 100000 120000
Threshold (us)
140000
160000
180000
200000
Variable Network Conditions
• Other traffic can change service rate.
• Need not even share same buffer.
• Nonlinear because of collisions.
• What happens when we add/remove competing stations?
Removing stations (4 → 1)
160000
20
140000
15
Queue Length (packets)
Drain Time (us)
120000
100000
80000
60000
40000
10
5
20000
0
230
232
234
236
238
Time (s)
240
242
244
0
230
232
234
236
238
Time (s)
240
242
244
Adding stations (4 → 8, ACK Prio)
700000
20
600000
15
Queue Length (packets)
Drain Time (us)
500000
400000
300000
10
200000
5
100000
0
100
120
140
160
Time (s)
Even base RTT changes.
180
200
0
100
120
140
160
Time (s)
180
200
Vegas in Practice
TargetCwnd = cwnd × baseRTT/minRTT
Make decision based on TargetCwnd − cwnd.
• Will Vegas make right decisions based on current RTT?
• Will Vegas get correct base RTT?
• Vary delay with dummynet.
• Vary BW by adding competing stations.
450
45
400
40
350
35
300
30
cwnd (packets)
Throughput (pps)
Vegas with 5ms RTT
250
200
25
20
150
15
100
10
50
5
0
0
0
20
40
60
80
Time (s)
100
120
140
Lower bound like 1 − α/cwnd.
0
20
40
60
80
Time (s)
100
120
140
Vegas with 200ms RTT
450
140
400
120
350
100
cwnd (packets)
Throughput (pps)
300
250
200
80
60
150
40
100
20
50
0
0
0
20
40
60
80
Time (s)
100
120
140
Sees loss and goes into Reno mode.
0
20
40
60
80
Time (s)
100
120
140
Conclusion
• With fixed traffic, delay is quite variable.
• Variability grows with buffer occupancy like
√
n.
• Obvious filters make things worse.
• Need to deal with change in traffic conditions.
• Linux Vegas does OK.
• Switch to Reno helps.
• Vegas insensitive at smaller buffer sizes.
• Variability at larger buffer sizes still a problem.