1. TCP-Friendly Congestion Control 2002.4.16 presented by Hyunjoo Kim

2.  TCP-friendly SIMD Congestion Control and Its Convergence Behavior Shudong Jin, Liang Guo, Ibrahim Matta, Acer Bestavros

3. Contents  Congestion control schemes AIMD Binomial algorithm TFRC TEAR  SIMD  Experimental Results  Conclusion

4. Congestion control  window-based schemes  equation-based schemes

5. Requirements for Congestion control  TCP-compatibility  TCP-friendliness  Smoothness  Aggressiveness  Responsiveness  Convergence

6. TCP-friendly congestion control schemes  AIMD  binomial algorithms  TFRC  TEAR

7. Binomial algorithms  nonlinear congestion control algorithm for Internet transport protocols and applications   k+l rule trade-off between aggressiveness, congestion responsiveness TCP-compatibility : k+l=1 and l  1 converge to fairness as long as k  0, l  0, k+l>0  IIAD Inverse-Increase/Additive decrease k = 1, l = 0

8. TFRC  TCP-Friendly Rate Control Protocol  equation-based congestion control  sequence number for measuring RTT  receiver feedback message for sender to measure RTT calculate loss event rate  sender calculate a new value for the allowed sending rate

9. TEAR  TCP emulation at receiver  hybrid approach  flow control for multimedia streaming  TEAR emulates the TCP sender ’ s flow control functions at receivers  determine the appropriate receiving rates of receivers based on congestion signals observed at the receiver (packet arrival, packet loss, timeout)  Sender sends data at reported rate

10. SIMD  Square-Increase/Multiplicative-Decrease  TCP-like window-based congestion control  improve transient behavior using history  self-clocking nature of window-based scheme, and simple modification of TCP

11. Control rules  AIMD  Binomial algorithm  SIMD

12. SIMD control rule  .... (1)  SIMD can grow aggressive with time

13. SIMD control rule  define  as (1) becomes..... (2)  Increase rule is proportional to  SIMD can be a special case of AIMD (  is always varying)  high smoothness using small   high aggressiveness when a sudden increase of available b.w.  better convergence behavior

14. Synchronized feedback assumption  by (Chiu and Jain)  all users sharing the same bottleneck will receive the same feedback  based on this feedback, the users try to adjust their load for sharing efficiently, and equally  synchronous feedback and control loop

15. Vector representation of a two-user case

16. Convergence of SIMD  fairness index : max (x1/x2, x2/x1)  bring the system to the intersection of the fairness line and the efficiency line (a) AIMD trajectory (a) SIMD trajectory

17.  SIMD < AIMD < IIAD in convergence time Convergence Speed (a) Increase Trajectory (b) AIMD vs SIMD (  =1/16)

18. Simulation Results  TCP-friendliness  TCP-Compatibility  Convergence to Fairness and Efficiency

19. TCP-friendliness Results  single flow, single fat link  drop packets w.p. p

20. TCP-Compatibility Results  n SIMD flows, n standard TCP SACK flows  4 background TCP flows to introduce random ACK delays TCP competing with SIMD(1/16), RED with ECN

21. TCP-Compatibility Results TCP competing with SIMD(1/16), RED without ECN

22. TCP-Compatibility Results TCP competing with SIMD(1/16), RED with DropTail

23. Simulation topology for convergence test

24. Convergence to Fairness Results (W1+W2=W, W1<W2) Two flows converge to fair share of bandwidth (a) TCP (b) AIMD(1/10, 1/16) (c) IIAD (d) SIMD(1/16)

25. Convergence to Efficiency Results (W1<W2<W/2) Two flows converge to fair share of bandwidth (a) TCP (b) AIMD(1/10, 1/16) (c) IIAD (d) SIMD(1/16)

26. Conclusion  window-based congestion control algorithm, SIMD  history information in control rules  multiplicative decrease, time square increase in window size  TCP-friendly, TCP-compatible under RED  faster convergence than memory-less algorithms

27.  A Memory-Based Approach for a TCP- Friendly Traffic Conditioner in DiffServ Networks K.R.R.Kumar, A.L.Ananda, LillyKutty Jacob

28. Contents  DiffServ  Memory Based Marker (MBM)  Experimental results  Conclusion

29. DiffServ  by IETF DWG (DiffServ Working Group)  scalable solution for providing service differentiation among flows  premium service  assured service (AS) target rate marking mechanism, queue management

30. RIO based scheme  RED with In/Out  Active Queue Management (AQM) at core router  differentiated dropping of packets during congestion  in-profile, out-profile

31. Traffic Conditioner  marking the packets as in-profile, out- profile at edge router  Token-Bucket (TB) based  avg. rate estimator based (Time Sliding Window (TSW) profile meter)

32. TB-based marking  measuring the amount of data that flows generate in any time interval  not easy to decide the optimal value of bucket size  if small, avg. packet rate of in-profile < target rage  if large, unfairness in bandwidth sharing

33. TSW profile meters (TSW-TC)  two components rate estimator avg. sending rate over time window (Tw) a marker  two approaches Tw is large cannot reflect the traffic dynamics of TCP Tw  RTT avg rate of in-profile packet is much more than the target rate in the under-subscribed scenario

34. Memory based marker  Design issue which understands the TCP dynamics which helps in reducing the influence of RTT and window size on TCP performance which reduce the burstiness of the marked/unmarked packes

35. MBM Marking algorithm  For each packet arrival If avg_rate  cir then mp = mp+(1-avg_rate/cir)+(par-avg_rate)/avg_rate; par = avg_rate; mark the packet using: cp 11 w.p. mp cp 00 w,p. (1-mp) else if avg_rate  cir then mp = mp+(par-avg_rate)/avg_rate; par = avg_rate; mark the packet using: cp 11 w.p. mp cp 00 w.p. (1-mp)

36. Simulation Scenario

37. Assured service for aggregates  2 sets of priority TCP flows(each having 6 micro flows)  a set of 9 best effort TCP micro flows

38. Effect of different RTT  5 pairs of flow aggregates (6 micro flows)  link bandwidth from R1 to R5 : 28Mbps

39. Effect of different window sizes  5 assured TCP flows having the same RTT (500ms)  target rate of 3Mbps  link bandwidth from R1 to R5 : 18 Mbps  optimum window size : 125 KB

40. Protection from best effort UDP flows  a set of priority TCP flows, a set of BE UDP and TCP flows  link bandwidth : 10 Mbps

41. Effect of UDP flows with target rates  a set of priority TCP, AS UDP flow with a target rate of 3 Mbps

42. Conclusion  memory-based approach in providing better quality of service for TCP flows  simplicity  least sensitivity to TCP and marker parameters  MBM helps in achieving target rate with a better fairness  better result using TCP extensions such as SACK

43. References  Shudong Jin, Liang Guo, Ibrahim Matta, Azer Bestavros, “ TCP-friendly SIMD Congestion Control and Its Convergence Behavior ”  K.R.R.Kumar, A.L.Ananda, Lillykutty Jacob, “ A Memory-based Approach for a TCP-Friendly Traffic Conditioner in DiffServ Networks ”  D.Bansal and H.Balakrishnan, “ Binomial congestion control algorithms ”, In Proceedings of IEEE INFOCOM, April 2001  S.Floyd, M.Handley, J.Padhye, J.Widmer, “ Equation-based congestion control for unicast applications ”, in Proceedings of ACM SIGCOMM, Aug 2000  I.Rhee, V.Ozdemir, Y.Yi., “ TEAR: TCP Emulation at Receivers – flow control for multimedia streaming ”, Technical report, Dept. of Computer Science, North Carolina State Univ. Apr. 2000  S.Blake, D.L.Black, M.Carlson, E.Davies, Z.Wang, and W.Weiss, “ An architecture for differentiated services ”, RFC 2475, Dec. 1998