1. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Improving TCP Performance in High Bandwidth High RTT Links Using Layered Congestion Control Sumitha Bhandarkar Saurabh Jain A. L. Narasimha Reddy Texas A & M University

2. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Layering Concepts Design Constraints –Fairness among flows of similar RTT –RTT unfairness no worse than TCP –Fair to TCP in slow networks Two dimensional congestion control –Increase layers, if no losses for extended period –Per-RTT window increase more aggressive at higher layers

3. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Layering –Start layering when window > W T –Associate each layer with a step size  K –When window increases from previous addition of layer by  K, increment number of layers –For each layer K, increase window by K per RTT Number of layers determined dynamically based on current network conditions. Layering Concepts (Cont.)

4. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu K Layering Concepts (Cont.) K + 1 K K - 1 Layer Number W K-1 Minimum Window Corresponding to the layer    Number of layers = K when W K  W  W K+1 WKWK W K+1

5. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Constraint 1 : –rate of increase for flow at higher layer should be lower than flow at lower layer Constraint 2 : –After a loss, recovery time for a larger flow should be more than the smaller flow (K 1 > K 2, for all K 1, K 2  2) Framework

6. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Decrease behavior : –Multiplicative decrease Increase behavior : –Additive increase with additive factor = layer number W = W + K/W A Design Choice

7. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu After loss, drop at most one layer Constraint for choice of  K : We choose  A Design Choice (Cont.)

8. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Choice of  : Since after loss, at most one layer is dropped,  (We choose  = 0.15 corresponding to K = 19) A Design Choice (Cont.)

9. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Time to claim bandwidth Analysis Speedup in Packet recovery time

10. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Steady state throughput where K ' is the layer corresponding to steady state window size,  is the window decrease factor and p is the steady state loss probability Analysis (Cont.)

11. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu RTT Unfairness –With random losses, RTT unfairness similar to TCP –With synchronized losses, RTT unfairness is –Can be easily compensated Modify increase behavior W = W + (K R * K) / W When K R  RTT (1/3), RTT unfairness similar to TCP When K R  RTT, linear RTT unfairness (window size independent of RTT) –Loss model depends on type of queue management, level of multiplexing etc. Analysis (Cont.)

12. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Window Comparison Experimental Evaluation

13. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Link Utilization Experimental Evaluation

14. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Fairness among multiple flows Experimental Evaluation

15. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Dynamic Link Sharing Experimental Evaluation

16. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Interaction with TCP Experimental Evaluation

17. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu RTT Unfairness Experimental Evaluation

18. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Why LTCP ? –Current design remains AIMD –Dynamically changes increase factor –Retains convergence and fairness properties –Simple to understand/implement –RTT unfairness similar to TCP Conclusions

19. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Characterize losses on actual high speed links Study alternate designs for LTCP framework Compare with other TCP based high speed solution. Preliminary results show –observed loss probability with LTCP is lower than other schemes –improved RTT unfairness –better TCP tolerance in high speed networks Future Work

20. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu RTT Unfairness Comparison with BIC (Preliminary Results)

21. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Questions ? Additional questions/feedback welcome at {sumitha,saurabhj,reddy}@ee.tamu.edu Thank You...

22. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Simulation Topology

23. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu HS-TCP Sally Floyd, “HighSpeed TCP for Large Congestion Windows”, RFC 3649 Dec 2003. Scalable TCP Tom Kelly, “Scalable TCP: Improving Performance in HighSpeed Wide Area Networks”, ACM Computer Communications Review, April 2003. FAST Cheng Jin, David X. Wei and Steven H. Low, “FAST TCP: motivation, architecture, algorithms, performance”, IEEE Infocom, March 2004. BIC Lisong Xu, Khaled Harfoush, and Injong Rhee, “Binary Increase Congestion Control for Fast Long-Distance Networks”, IEEE Infocom, March 2004. HTCP R. N. Shorten, D. J. Leith, J. Foy, and R. Kilduff, “H-TCP Protocol for High-Speed Long Distance Networks”, PFLDnet 2004, February 2003. Related Work

24. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Probability of loss for LTCP Probability of loss for TCP RTT Fairness(Random Loss Model)

25. Texas A&M University {sumitha,saurabhj,reddy}@ee.tamu.edu Observed Loss Rates Comparison with BIC (Preliminary Results) Single Flow, 1Gbps bottleneck link