On Individual and Aggregate TCP Performance

1. On Individual and Aggregate TCP Performance Lili Qiu Yin Zhang Srinivasan Keshav Cornell University 7th International Conference on Network ProtocolsToronto, Canada, October 31 - November 3, 1999

2. Talk Outline • Introduction & Motivation • Brief Overview of TCP-Reno • Related Work • Aggregate TCP performance • Summary and future work

3. Introduction • Understanding TCP performance is critical • Our knowledge is still insufficient • TCP performance under many competing flows is not sufficiently explored • Unclear about the impact of network topology • Goal • Investigate both individual and aggregate TCP behavior under many competing connections • Investigate the impact of network topology • Approach • Extensive simulation

4. Overview of TCP-Reno • Slow start • Congestion avoidance • Loss recovery • Time out • Fast retransmission

5. Related Work • TCP analytical model [Padhye et al 98] • Models the throughput of an individual TCP conn • Our simulation evaluation shows the model is reasonably accurate • Doesn’t consider aggregate performance • TCP Behavior with many TCP Flows [Morris 97] • TCP-Taheo under RED dropping policy • Only studies the impact of different # conns • Doesn’t consider other network parameters

6. Aggregate TCP Performance • Motivation • Useful for network provisioning • Goal • Aggregate TCP behavior for a large number of connections • Impact of network topology

7. Network Model • Network parameters • Bandwidth • Prop delay • Buffer size • Total number of connections • TCP-Reno • Dropping tail • Notation • = bottleneck bandwidth * propagation delay • = + bottleneck buffer

8. Simulation Methodology • Three sets of simulations • Same RTT • With random processing time • Two RTT’s

9. Overview of Our Results

10. Overview of Our Results (Cont’d)

11. Simulation 1: With Same RTT • TCP exhibits wide range of behaviors depending on • Case 1: (Large Pipe) • Case 2: (Small Pipe) • Case 3: (Medium Pipe)

12. Simulation 1: With Same RTTCase 1 ( ) • Global synchronization • Fair

13. Simulation 1: With Same RTTCase 2 ( ) • Shut-off connections

14. Simulation 1: With Same RTTCase 3: ( ) • Local synchronization

15. Simulation 1: With Same RTTPerformance Results • Throughput • Close to 1 if buffer > Wopt or # conn is large • Goodput • Decreases with # conn • Decreasing rate depends on bottleneck bandwidth • Loss probability • Small # conn: Quadratic • Large # conn: Hyperbolic

16. Simulation 2: With Random Processing Time • Case 1 ( ) • Global synchronization breaks down • Case 2 ( ) • Discrimination less severe • Fewer shut-off connections • Case 3 ( ) • Local synchronization disappears

17. Simulation 2: With Random Processing TimePerformance Results • Aggregate Throughput • Aggregate Goodput • Loss Probability • Small # conns: linear increase • Large # conns: hyperbolic as before

18. Simulation 3: Different RTTs • It’s well-known that TCP has bias against long roundtrip time connections • Goal: Quantify the discrimination • Simulation Topology:

19. Simulation 3:Two RTT’s with Random Processing Time

20. Summary and Future Work • Evaluate the analytical model for individual TCP connection • Study aggregate TCP performance • With same RTT • With random processing time • With two RTT’s and random processing time • Future directions • Use Internet experiments to verify the results • Further explore TCP performance under different RTT’s