On Individual and Aggregate TCP Performance Lili Qiu Yin Zhang Srinivasan Keshav Cornell University 7th International Conference on Network Protocols Toronto, 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 Same RTT Global sync Shut-off conns Local sync Random Proc. Time Remove global sync Fewer shut-off conns Remove local sync Two RTT

10 Overview of Our Results (Cont’d) ThroughputClose to 1 unless buffer < or # conns is small GoodputDecreases with # conns Decreasing rate depends on bottleneck bandwidth Loss ProbabilitySame RTTWith Random proc time Few # conns QuadraticLinear Many # conns Hyperbolic

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 RTT Case 1 ( ) Global synchronization Fair

13 Simulation 1: With Same RTT Case 2 ( ) Shut-off connections

14 Simulation 1: With Same RTT Case 3: ( ) Local synchronization

15 Simulation 1: With Same RTT Performance 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 Time Performance 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