1. 1 MaxNet and TCP Reno/RED on mice traffic Khoa Truong Phan Ho Chi Minh city University of Technology (HCMUT)

2. Faculty of Computer Science and Engineering – HCMUT 2 Outline  Introduction  Overview of TCP Congestion Control  TCP Reno/RED and MaxNet TCP  Experiment and Evaluation

3. Faculty of Computer Science and Engineering – HCMUT 3 Introduction Figure 1. Traffic jam → Traffic on the Internet will be like this if we don’t have an efficient mechanism to avoid congestion.

4. Faculty of Computer Science and Engineering – HCMUT 4 Overview of TCP Congestion Control Congestion collapse TCP Vegas 1986 1988 TCP Tahoe 1990 199319962003 TCP Reno TCP NewReno FAST TCP 2006 Maxnet TCP Figure 2. History of TCP Congestion Control Algorithms

5. Faculty of Computer Science and Engineering – HCMUT 5 ACK packets` Data packets TCP receiver TCP sender PP p1p1 p2p2 P’’ PP’ Router Source compute rate to transmit Link mark/drop packets P’’=P’+ P 1 P=P’’+ P 2 P’ P’’ PP P Rate P= ∑P i Sink sends ACK to source Overview of TCP Congestion Control (cont) Figure 3. TCP Congestion Control model

6. Faculty of Computer Science and Engineering – HCMUT 6 TCP Reno Figure 4. Demand function of TCP Reno AIMD (Additive Increase Multiplicative Decrease) mechanism:

7. Faculty of Computer Science and Engineering – HCMUT 7 TCP Reno (cont) Figure 5. Operation mode of TCP Reno

8. Faculty of Computer Science and Engineering – HCMUT 8 RED router (1)(2) (3) b min b max 2b ma x RED router defines two thresholds in the buffer: b min and b max. The probability of marking/dropping (p) as follows: Figure 6. Operation mode of RED router

9. Faculty of Computer Science and Engineering – HCMUT 9 TCP Reno/RED  Assuming that sending rate at source is and router drops the packets at the probability of. Every drop packet causes a negative ACK.  Based on AIMD, source increases window size by 1/w for each positive ACK and decreases window size by half for each negative ACK.  At equilibrium, window size adjustment equal to zero  From the dropping scheme of RED, each source always have backlog at least at one router.  Window size adjustment:

10. Faculty of Computer Science and Engineering – HCMUT 10 TCP Reno/RED (cont) Elephant traffic Mice traffic RED router Figure 7. Queuing delay of RED router

11. Faculty of Computer Science and Engineering – HCMUT 11 MaxNet TCP Source: Router: Figure 8. Operation mode of TCP Reno Demand function: µ < 100%

12. Faculty of Computer Science and Engineering – HCMUT 12 MaxNet TCP (cont) Figure 9. Operation mode of MaxNet TCP MaxStart Figure 10. Queuing delay of RED router

13. Faculty of Computer Science and Engineering – HCMUT 13 Experiment Figure 11. Experiment test bed  Pentium IV PCs (CPU 1.8GHz, 512MB RAM) are used  Dummynet router is used to configured end-to-end delay at 20ms

14. Faculty of Computer Science and Engineering – HCMUT 14 Experiment (cont)  Monitoring the queue at MaxNet router and Reno/RED router Figure 13(a). Queue at MaxNet router Figure 13(b). Queue at Reno/RED router

15. Faculty of Computer Science and Engineering – HCMUT 15 Experiment (cont)  Response time of HTTP connections in MaxNet and Reno/RED Figure 14. Response time of HTTP in Reno/RED vs. MaxNet

16. Faculty of Computer Science and Engineering – HCMUT 16 Experiment (cont) Figure 12(a). 1 FTP and 50 HTTP connections Figure 12(a). 1 FTP and 100 HTTP connections  Impact of new HTTP connections on throughput of elephant traffic Reno/RED and MaxNet

17. Faculty of Computer Science and Engineering – HCMUT 17 Conclusions  MaxNet clears buffer while Reno/RED always keeps a backlog in routers.  MaxNet has shorter response time for mice traffic than Reno/RED.  Arrival mice flows cause packet loss which degrades the throughput of elephant traffic.  MaxStart mechanism of MaxNet, using multi-bit signaling, controls mice flows to the target rate more quickly than TCP Reno.  For using MaxNet, source hosts, intermediate routers and destination hosts need to be upgraded.

18. Faculty of Computer Science and Engineering – HCMUT 18 References  MaxNet homepage: www.netlab.caltech.edu/maxnet  Duc Nguyen, Jidong Wang, Lachlan L. H. Andrew and Sammy Chan, “MaxNet: A More Efficient Max-min Fair Allocation Scheme”, in Proc. Intl. Teletraffic Congress-19, Beijing China, 2005.  Bartek Wydrowski, Lachlan L.H. Andrew, Moshe Zukerman, "MaxNet: A Congestion Control Architecture for Scalable Networks",IEEE Communications Letters, vol. 7, no. 10, Oct. 2003, pp. 511 -513.  Bartek Wydrowski, Lachlan L.H. Andrew, Iven M. Y. Mareels, "MaxNet: Faster Flow Control Convergence", NETWORKING 2004: 588-599.

19. Faculty of Computer Science and Engineering – HCMUT 19 THANK YOU!

20. Faculty of Computer Science and Engineering – HCMUT 20 Demand function of MaxNet →  Achieve Max-min fairness  Stability

21. Faculty of Computer Science and Engineering – HCMUT 21 MaxMin Fairness MaxMin fairness allocation

22. Faculty of Computer Science and Engineering – HCMUT 22 Fairness bandwidth of MaxNet vs. Reno MaxNet TCP Reno

23. Faculty of Computer Science and Engineering – HCMUT 23 Elephant traffic MaxNet vs. TCP Reno MaxNet Reno

24. Faculty of Computer Science and Engineering – HCMUT 24 Throughput of MaxNet Throughput of Reno