Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Design Considerations for RINA Congestion
Control over WiFi Links
Kristian A. Hiorth1, Michael Welzl1
University of Oslo, ...
Design Considerations for RINA Congestion Control over
WiFi Links
Kristian A. Hiorth
Dept. of Informatics
University of Os...
Overview: we will describe the background for our work,
examine measurement results and discuss benefits of our
proposed co...
Background and motivation
Measurements
Benefits
Wireless networks break the assumptions baked into
traditional Internet congestion control
2
Wireless networks break the assumptions baked into
traditional Internet congestion control
2
Wireless networks break the assumptions baked into
traditional Internet congestion control
[Photos: “Pete” and Andre Douqu...
The IEEE 802.11 Distributed Coordination Function
ultimately determines sending rates over WiFi
[Diagram: Wikipedia]
3
The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed fra...
The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed fra...
The DCF has been extensively studied, however models
assume numerous constraints
Example assumptions:
Saturation
Fixed fra...
To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check ...
To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check ...
To overcome model limitations, we envision a measurement
driven machine learning solution
Measure → Predict → Cross-check ...
RINA is scope-aware and naturally enables localized
congestion control loops
Application
Link Link
Link Link
Routing
6
Performance Enhancing Proxies allow establishing local
control loops even in IP networks
Wireless Local Area Network
Wirel...
Performance Enhancing Proxies allow establishing local
control loops even in IP networks
Wireless Local Area Network
Wirel...
Background and motivation
Measurements
Benefits
We studied WiFi DCF behaviour by measuring with real
hardware using a simple scenario
Wireless Local Area Network
Node 3 (...
When all stations send at the same physical rate, DCF
behaves very predictably and fairly
20 25 30 35 40 45
Time (seconds)...
DCF behaviour remains highly predictable also when
allowing different PHY rates
20 25 30 35 40 45
Time (seconds)
0.00
0.25
...
Normal PHY rate adaptation introduces more noise, yet
appears reasonaby predictable
20 25 30 35 40 45
Time (seconds)
0.0
0...
Relying directly on the DCF enhances performance
compared to TCP congestion control
20 25 30 35 40 45
Time (seconds)
0.0
0...
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
T...
Background and motivation
Measurements
Benefits
Even in simple wireless LANs a predictive solution is
beneficial compared to plain flow control
[Photo: joonas.fi]
14
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 H...
Wireless mesh networks can benefit greatly from the use of
a known-rate, hop by hop congestion control
[Photo: DeWALT]
16
Unlike many previously proposed cross-layer mechanisms our
concept is properly scoped
Wireless LAN
WirelessDesktop
Network...
Unlike many previously proposed cross-layer mechanisms our
concept is properly scoped
Wireless LAN
WirelessDesktop
Network...
In conclusion: Local loop, data-driven WiFi congestion
control appears both feasible and superior to end-to-end
Predictive...
In conclusion: Local loop, data-driven WiFi congestion
control appears both feasible and superior to end-to-end
Predictive...
Design Considerations for RINA Congestion Control over WiFi Links
You’ve finished this document.
Download and read it offline.
Upcoming SlideShare
What to Upload to SlideShare
Next
Upcoming SlideShare
What to Upload to SlideShare
Next
Download to read offline and view in fullscreen.

Share

Design Considerations for RINA Congestion Control over WiFi Links

Download to read offline

Design Considerations for RINA Congestion Control over WiFi Links

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all
  • Be the first to like this

Design Considerations for RINA Congestion Control over WiFi Links

  1. 1. Design Considerations for RINA Congestion Control over WiFi Links Kristian A. Hiorth1, Michael Welzl1 University of Oslo, Norway1 February 18, 2019
  2. 2. Design Considerations for RINA Congestion Control over WiFi Links Kristian A. Hiorth Dept. of Informatics University of Oslo [Photo: Andre Douque]
  3. 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. 4. Background and motivation Measurements Benefits
  5. 5. Wireless networks break the assumptions baked into traditional Internet congestion control 2
  6. 6. Wireless networks break the assumptions baked into traditional Internet congestion control 2
  7. 7. Wireless networks break the assumptions baked into traditional Internet congestion control [Photos: “Pete” and Andre Douque] 2
  8. 8. The IEEE 802.11 Distributed Coordination Function ultimately determines sending rates over WiFi [Diagram: Wikipedia] 3
  9. 9. The DCF has been extensively studied, however models assume numerous constraints Example assumptions: Saturation Fixed frame size Fixed MAC settings 4
  10. 10. The DCF has been extensively studied, however models assume numerous constraints Example assumptions: Saturation Fixed frame size Fixed MAC settings 4
  11. 11. The DCF has been extensively studied, however models assume numerous constraints Example assumptions: Saturation Fixed frame size Fixed MAC settings 4
  12. 12. To overcome model limitations, we envision a measurement driven machine learning solution Measure → Predict → Cross-check with buffer drainage 5
  13. 13. To overcome model limitations, we envision a measurement driven machine learning solution Measure → Predict → Cross-check with buffer drainage 5
  14. 14. To overcome model limitations, we envision a measurement driven machine learning solution Measure → Predict → Cross-check with buffer drainage 5
  15. 15. RINA is scope-aware and naturally enables localized congestion control loops Application Link Link Link Link Routing 6
  16. 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. 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. 18. Background and motivation Measurements Benefits
  19. 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. 20. When all stations send at the same physical rate, DCF behaves very predictably and fairly 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 PHY fixed at 54Mbps 9
  21. 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. 22. Normal PHY rate adaptation introduces more noise, yet appears reasonaby predictable 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 PHY controlled by Minstrel rate adaption algorithm. 11
  23. 23. Relying directly on the DCF enhances performance compared to TCP congestion control 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 Cubic 12
  24. 24. 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
  25. 25. Background and motivation Measurements Benefits
  26. 26. Even in simple wireless LANs a predictive solution is beneficial compared to plain flow control [Photo: joonas.fi] 14
  27. 27. 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
  28. 28. Wireless mesh networks can benefit greatly from the use of a known-rate, hop by hop congestion control [Photo: DeWALT] 16
  29. 29. 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
  30. 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. 31. 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
  32. 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

Design Considerations for RINA Congestion Control over WiFi Links

Views

Total views

1,003

On Slideshare

0

From embeds

0

Number of embeds

730

Actions

Downloads

8

Shares

0

Comments

0

Likes

0

×