Design Considerations for RINA Congestion Control over WiFi Links

1. Design Considerations for RINA Congestion
Control over WiFi Links
Kristian A. Hiorth1, Michael Welzl1
University of Oslo, Norway1
February 18, 2019

2. Design Considerations for RINA Congestion Control over
WiFi Links
Kristian A. Hiorth
Dept. of Informatics
University of Oslo
[Photo: Andre Douque]

3. Overview: we will describe the background for our work,
examine measurement results and discuss benefits of our
proposed concept
Background
20 25 30 35 40 45
Time (seconds)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
Estimate
Measurements
Benefits
1

4. Background and motivation
Measurements
Benefits

5. Wireless networks break the assumptions baked into
traditional Internet congestion control
2

6. Wireless networks break the assumptions baked into
traditional Internet congestion control
2

7. Wireless networks break the assumptions baked into
traditional Internet congestion control
[Photos: “Pete” and Andre Douque]
2

8. The IEEE 802.11 Distributed Coordination Function
ultimately determines sending rates over WiFi
[Diagram: Wikipedia]
3

9. The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed frame size
Fixed MAC settings
4

10. The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed frame size
Fixed MAC settings
4

11. The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed frame size
Fixed MAC settings
4

12. To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check with buffer drainage
5

13. To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check with buffer drainage
5

14. To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check with buffer drainage
5

15. RINA is scope-aware and naturally enables localized
congestion control loops
Application
Link Link
Link Link
Routing
6

16. Performance Enhancing Proxies allow establishing local
control loops even in IP networks
Wireless Local Area Network
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
Access Point WAN
PEP
7

17. Performance Enhancing Proxies allow establishing local
control loops even in IP networks
Wireless Local Area Network
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
Access Point WAN
PEP
7

18. Background and motivation
Measurements
Benefits

19. We studied WiFi DCF behaviour by measuring with real
hardware using a simple scenario
Wireless Local Area Network
Node 3 (measurement node)
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
Node 1
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
Node 2
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
Access Point “WAN”
8

20. When all stations send at the same physical rate, DCF
behaves very predictably and fairly
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Time (seconds)
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1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
Estimate
PHY fixed at 54Mbps
9

21. DCF behaviour remains highly predictable also when
allowing different PHY rates
20 25 30 35 40 45
Time (seconds)
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
PHY fixed at 12Mbps, 24Mbps and 54Mbps, respectively.
10

22. Normal PHY rate adaptation introduces more noise, yet
appears reasonaby predictable
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Time (seconds)
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1.0
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Total
Flow 1
Flow 2
Flow 3
PHY controlled by Minstrel rate adaption algorithm.
11

24. Relying directly on the DCF enhances performance
compared to TCP congestion control
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Time (seconds)
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1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
Estimate
Pure DCF
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Time (seconds)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
TCP Cubic
12

25. Even TCP BBR is outperformed
20 25 30 35 40 45
Time (seconds)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
Estimate
Pure DCF
20 25 30 35 40 45
Time (seconds)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Throughput(bitspersecond)
×107
Total
Flow 1
Flow 2
Flow 3
TCP BBR
13

26. Background and motivation
Measurements
Benefits

27. Even in simple wireless LANs a predictive solution is
beneficial compared to plain flow control
[Photo: joonas.fi]
14

28. In RINA there are obvious benefits to knowing the actual
attainable link rate
Host
W
iFi shi
m
App
1st hop AP
2
nd hop AP Host
15

29. Wireless mesh networks can benefit greatly from the use of
a known-rate, hop by hop congestion control
[Photo: DeWALT]
16

30. Unlike many previously proposed cross-layer mechanisms our
concept is properly scoped
Wireless LAN
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
AP WAN WAN
bottleneck!
17

31. Unlike many previously proposed cross-layer mechanisms our
concept is properly scoped
Wireless LAN
WirelessDesktop
NetworkCard
OK
Madein
Groland
w_i
~~ø#|||
1121314156---**788
AP WAN WAN
bottleneck!
17

32. In conclusion: Local loop, data-driven WiFi congestion
control appears both feasible and superior to end-to-end
Predictive, quantified rate
+
Proper scoping
↓
Optimized WiFi performance
Questions?
18

33. In conclusion: Local loop, data-driven WiFi congestion
control appears both feasible and superior to end-to-end
Predictive, quantified rate
+
Proper scoping
↓
Optimized WiFi performance
Questions?
18