1. Aditya Akella (aditya@cs.cmu.edu) Exploring Congestion Control Aditya Akella With Srini Seshan, Scott Shenker and Ion Stoica

2. Aditya Akella (aditya@cs.cmu.edu) Early Congestion Control Influences on early congestion control design Chiu-Jain analysis AIMD most fair, stable and efficient Loss recovery mechanism Reno-style Large penalty on over-shooting Simple FIFO drop-tail routers

3. Aditya Akella (aditya@cs.cmu.edu) Motivation for Our Study Improvements TCP loss recovery SACK Drop and scheduling policies at routers AQM ECN Flow-level fairness DRR

4. Aditya Akella (aditya@cs.cmu.edu) Questions.. Is AIMD still the only choice? What other linear policies are viable?

5. Aditya Akella (aditya@cs.cmu.edu) Outline of the Talk Motivation for evaluation methodology Extreme cases The methodology Results Hybrid algorithms Summary

6. Aditya Akella (aditya@cs.cmu.edu) Can There Ever be a Clear Winner? Possibly not… AIMDAIADMIMDMIAD 0.970.930.610.95 AIMDAIADMIMDMIAD 0.520.960.820.75

7. Aditya Akella (aditya@cs.cmu.edu) Evaluation Methodology: Motivation No single algorithms is superior Meaningful comparison is tough Guiding principles Algorithms should not be designed for specific scenario(s) Robustness more important than optimality Aim is to identify key aspects not to pick winners

8. Aditya Akella (aditya@cs.cmu.edu) Methodology Motivation from competitive analysis A – set of algorithms we wish to compare A = E – set of environments the algorithms in A might be faced with

9. Aditya Akella (aditya@cs.cmu.edu) Methodology Contd.. Rank measures worst-case behavior Average measures mean behavior

10. Aditya Akella (aditya@cs.cmu.edu) Choosing A and E A – limited set of algorithms Proven ‘good’ via simulations E – include wide variety while keeping size small Some deliberately extreme Some to study key aspects Other to be realistic (for now)

11. Aditya Akella (aditya@cs.cmu.edu) Outline of Results Impact of Loss Recovery Reno-style SACK-style Impact of router queuing behavior Effect of RED Effect of ECN Effect of DRR Discussion

12. Aditya Akella (aditya@cs.cmu.edu) Reno-style Loss Recovery AIMD and AIAD provide identical goodput performance AIMD is the only fair algorithm AIMD had the best delay and loss rates too Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.070.010.0916.440.00 AIAD0.030.010.4631.390.46 MIMD0.340.220.140.130.86 MIAD0.400.210.290.190.52

13. Aditya Akella (aditya@cs.cmu.edu) SACK-style Loss Recovery All schemes except MIAD provide reasonable goodput performance AIMD is the only fair algorithm. Fairness, loss rates, delays of others worsen Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.190.030.065.730.00 AIAD0.140.010.9929.742.06 MIMD0.160.031.034.991.41 MIAD0.460.160.8417.443.99

14. Aditya Akella (aditya@cs.cmu.edu) Effect of RED + Reno-style Recovery AIMD and AIAD provide best goodput performance Fairness of all algorithms improves Loss rates and delays are low for all schemes Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.060.010.104.340.00 AIAD0.060.010.1710.390.84 MIMD0.250.090.111.930.45 MIAD0.370.130.119.811.36

15. Aditya Akella (aditya@cs.cmu.edu) Effect of RED + SACK-style Recovery AIAD provides best goodput performance and is reasonably fair. Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.170.04 1.860.00 AIAD0.00 0.3312.391.70 MIMD0.250.060.242.190.69 MIAD0.480.160.8912.202.88

16. Aditya Akella (aditya@cs.cmu.edu) Effect of ECN Either form of loss recovery (e.g., SACK, shown below) MIAD, MIMD and AIAD provide best goodput performance AIMD provides worst goodput performance AIMD has the best fairness, delay and loss rate Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.260.060.041.550.00 AIAD0.220.030.5314.661.21 MIMD0.150.050.382.490.56 MIAD0.040.010.8331.091.87

17. Aditya Akella (aditya@cs.cmu.edu) Effect of DRR Either form of loss recovery (e.g., SACK, shown below) Same ordering as with drop-tail buffers All algorithms are now fair Reno Drop Tail GoodputFairnessDelayLoss CDDDD AIMD0.030.010.1120.310.00 AIAD0.040.010.1022.711.13 MIMD0.020.000.3017.081.90 MIAD0.360.130.225.823.61

18. Aditya Akella (aditya@cs.cmu.edu) Putting It All Together

19. Aditya Akella (aditya@cs.cmu.edu) Reading into the Results AIMD is the best if we want Great fairness Low loss and delay Reasonable goodput AIMD is not always supreme if we want Reasonable fairness, loss and delay Maximum goodput But… AIAD is a always a leading goodput performer

20. Aditya Akella (aditya@cs.cmu.edu) A Closer Look at AIAD AIAD’s weakness Unfair at times (FIFO drop-tail setting) Otherwise shows good performance How can we cure the AIAD’s unfairness? Hybrid algorithms

21. Aditya Akella (aditya@cs.cmu.edu) Hybrid Algorithms AIMD etc. are pure linear algorithms Hybrid algorithms allow both additive and multiplicative components How can the unfairness of AIAD be fixed? Hybrid schemes are the answer to AIAD’s unfairness

22. Aditya Akella (aditya@cs.cmu.edu) Fairness and Hybrid Schemes Theorem: An algorithm converges to fairness as long as it is not purely additive (both increase and decrease are additive) or purely multiplicative (both increase and decrease are multiplicative) Caveat: This does not consider unstable schemes (like MIAD)

23. Aditya Akella (aditya@cs.cmu.edu) Getting Back to AIAD How can we cure AIAD? Add a small multiplicative component to the decrease A-I-M-A-D (additive increase, multiplicative additive decrease) AIMAD provides Good convergence to fairness Better loss and delay Identical goodput performance

24. Aditya Akella (aditya@cs.cmu.edu) Hybrid Schemes – Results AIMAD (AIAD with multiplicative component (0.9) in decrease) MAIMD (AIMD with multiplicative component (1.1) in increase)

25. Aditya Akella (aditya@cs.cmu.edu) What did Chiu-Jain Say? Chiu-Jain do not allow additive component a < 0 in decrease But our theorem allows AIMAD which has a < 0 The catch Chiu-Jain’s conditions are sufficient but not necesary

26. Aditya Akella (aditya@cs.cmu.edu) Summary Tested the four basic linear alternatives under a variety of situations Our work in a line “ If an alternate world were to choose a congestion control algorithm, is AIMD the only possible choice? Our answer is no”.