![Design Considerations for RINA Congestion Control over
WiFi Links
Kristian A. Hiorth
Dept. of Informatics
University of Oslo
[Photo: Andre Douque]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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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 bene๏ฌts 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) ร107 Total Flow 1 Flow 2 Flow 3 Estimate Measurements Bene๏ฌts 1
- 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: โPeteโ 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 โ Predict โ Cross-check with bu๏ฌer drainage 5
- 13. To overcome model limitations, we envision a measurement driven machine learning solution Measure โ Predict โ Cross-check with bu๏ฌer drainage 5
- 14. To overcome model limitations, we envision a measurement driven machine learning solution Measure โ Predict โ Cross-check with bu๏ฌer 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 ~~รธ#||| 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 ~~รธ#||| 1121314156---**788 Access Point WAN PEP 7
- 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 ~~รธ#||| 1121314156---**788 WirelessDesktop NetworkCard OK Madein Groland w_i ~~รธ#||| 1121314156---**788 Node 1 WirelessDesktop NetworkCard OK Madein Groland w_i ~~รธ#||| 1121314156---**788 Node 2 WirelessDesktop NetworkCard OK Madein Groland w_i ~~รธ#||| 1121314156---**788 Access Point โWANโ 8
- 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) ร107 Total Flow 1 Flow 2 Flow 3 Estimate PHY ๏ฌxed at 54Mbps 9
- 21. DCF behaviour remains highly predictable also when allowing di๏ฌerent 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) ร107 Total Flow 1 Flow 2 Flow 3 PHY ๏ฌxed at 12Mbps, 24Mbps and 54Mbps, respectively. 10
- 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) ร107 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 20 25 30 35 40 45 Time (seconds) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Throughput(bitspersecond) ร107 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) ร107 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) ร107 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) ร107 Total Flow 1 Flow 2 Flow 3 TCP BBR 13
- 27. Even in simple wireless LANs a predictive solution is bene๏ฌcial compared to plain ๏ฌow control [Photo: joonas.๏ฌ] 14
- 28. In RINA there are obvious bene๏ฌts 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 bene๏ฌt 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 ~~รธ#||| 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 ~~รธ#||| 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, quanti๏ฌed rate + Proper scoping โ 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, quanti๏ฌed rate + Proper scoping โ Optimized WiFi performance Questions? 18