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Inline Path Characteristic Estimation to Improve TCP Performance in High Bandwidth-Delay Networks

Inline Path Characteristic Estimation to Improve TCP Performance in High Bandwidth-Delay Networks. Cesar Marcondes Anders Persson Medy Sanadidi Mario Gerla. HIDEyuki Shimonishi Takayuki Hama Tutomu Murase. Outline. Inferring Path Characteristics Bandwidth Estimation Buffer Estimation

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Inline Path Characteristic Estimation to Improve TCP Performance in High Bandwidth-Delay Networks

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  1. Inline Path Characteristic Estimationto Improve TCP Performancein High Bandwidth-Delay Networks Cesar Marcondes Anders Persson Medy Sanadidi Mario Gerla HIDEyuki Shimonishi Takayuki Hama Tutomu Murase

  2. Outline • Inferring Path Characteristics • Bandwidth Estimation • Buffer Estimation • Description of Testbed • Improving TCP Performance using Inline Path Estimations • TCP Westwood BBE (Buffer and Bandwidth Estimation) • Conclusions and Future Work

  3. Motivation- Inferring Path Characteristics - Buffer Estimation Bandwidth Estimation Loss discrimination: Efficiency in leaky pipes Link capacity: Efficiency in large pipes Congestion detection: Friendliness to TCP-Reno Design novel protocols that make wise usage of estimations

  4. Internet Measurement Testbed Topology(Transpacific Link)

  5. Pathrate - Active (Infocom 2001) dispersion of packet pairs and packet trains, uncover possible capacity modes, apply statistic analysis to choose Capprobe - Active (Sigcomm 2004) - Packet pair measurement with filtering out delayed samples Capacity Estimation TCP Inline Measurements • TCP-Westwood • (Global Internet 2005) • - Packet pair like capacity estimation using TCP ACK packets • TCPProbe(Global Internet 2005) • - TCP inline version of CapProbe, it flips packets to obtain packet pairs in a delayed ACK scenario 5 3 4 2 1

  6. Measurement results (1)Off-line Measurement Convergence Speed 6sec 12sec 70sec

  7. Measurement results (2)TCP Inline Measurement Accuracy Convergence Original TCPW ≈ 60Mbps < 1sec TCPProbe ≈ 80Mbps 32sec

  8. Buffer Estimation • Idea: Correlate the loss of a packet with the RTT observed just before the loss • Buffer size estimation (RTT value a packet loss likely to happen) • Congestion estimation, loss discrimination (position of current RTT) Buffer size 15msec

  9. Measurement results (1)Off-line measurement for buffer capacity • Traceroute + Heavy Cross-Traffic Measurements • track down the location of bottleneck and observed an increase of 15msec on the average ICMP delay • RTT histogram of TCP flows Buffer size = 15msec High load >80% Aggregate 10 Flows Low load <10% Single Flow Overall loss rate = 0.005% Loss rate (RTT<147msec)= 0.005% Random loss ?

  10. Measurement results (2)Off-line measurement for random losses • Simulation Results • 135msec RTT + 15msec buffer • 80Mbps bottleneck like • 4.9 * 10-5 uniform dist. random loss 1 flow Simulation 17Mbps Measurement 15Mbps 10 aggregated flows Simulation 72Mbps Measurement 65Mbps • Low Rate UDP Measurement • 1-3Mbps UDP CBR traffic •  Loss rate = 0.001% 0.001-0.005% should be a good numberfor random loss rate

  11. TCP Westwood BBE • Optimize tradeoff between efficiency and friendliness to TCP-Reno • Robustness to buffer capacity variations and RTT variations • Optimize window reduction upon a packet loss, using • In line bandwidth estimation • In line buffer estimation

  12. TCPW-BBE Buffer Estimation • RTT dynamic range • RTTmin: minimum RTT (= propagation delay) • RTTcong: RTT value when packet losses likely to happen • Congestion = position of current RTT in the range RTTcong RTTmin RTTcong is an exponential average ofRTT right before losses 1 Congestion level c = RTT – RTTmin Losses due to congestion RTTcong – RTTmin Losses due to error 0 RTT before packet losses [msec]

  13. Congestion Window Reduction Congestion window reductionafter a packet loss 1 0.5 TCP-Reno Original TCPW TCPW-BBE Congestion 0 1 (RTT=2xRTTmin) Should be a random loss Smaller reduction Should be a congestion loss Halve the window

  14. Measurement Result (1)Buffer estimation and loss discrimination • Buffer Estimation • Inline estimating of buffer around 15 msec • * consistent with previous results • Inline Loss Discrimination • Window reduction depending on the congestion level estimated • * more robust to non-congestion errors

  15. Measurement Result (2)Efficiency and Friendliness Average Throughput of Multiple Trials Pathload Estimation: 61-74Mbps Avail. Conjectured Non-Congestion Error: 10-5 Cumulative Throughput during 2000 Secs Pathload Estimation : 30Mbps Avail. More Congestion losses

  16. Conclusions and Future Work • Path Characteristic Estimations as presented in this paper are reliable/fast/non-intrusive and present a major advantage for future Intelligent Network Stacks • In our measurement study, we presented, in addition to extensive Internet results, cross-validation based on more than one source and we observed that the inline capacity and buffer estimations are reliable. • In the specific scenario tested, TCP BBE was able to improve the performance when comparing to NewReno on a non-congested scenario • In the future, a combination of BE (Bandwidth Estimation from TCP Westwood) and TCPProbe can lead to faster capacity estimation. Additionally, new methods of using such estimates to improve start-up, cross-traffic identification, changes in route characteristics and ameliorate burstiness issues using buffer-awareness ought to be studied

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