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Maximizing End-to-End Network Performance

Maximizing End-to-End Network Performance. Thomas Hacker University of Michigan October 26, 2001. Introduction. Applications experience network performance from a end customer perspective Providing end-to-end performance has two aspects Bandwidth Reservation Performance Tuning

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Maximizing End-to-End Network Performance

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  1. MaximizingEnd-to-End Network Performance Thomas Hacker University of Michigan October 26, 2001

  2. Introduction • Applications experience network performance from a end customer perspective • Providing end-to-end performance has two aspects • Bandwidth Reservation • Performance Tuning • We have been working to improve actual end-to-end throughput using Performance Tuning • This work allows applications to fully exploit reserved bandwidth

  3. Improve Network Performance • Poor network performance arises from a subtle interaction between many different components at each layer of the OSI network stack • Physical • Data Link • Network • Transport • Application

  4. TCP Bandwidth Limits – Mathis Equation • Based on characteristics from physical layer up to transport layer. • Hard Limits • TCP Bandwidth, Max Packet Loss

  5. Packet Loss and MSS • If the minimum link bandwidth between two hosts is OC-12 (622 Mbps), and the average round trip time is 20 msec, the maximum packet loss rate necessary to achieve 66% of the link speed (411 Mbps) is approximately 0.00018%, which represents only 2 packets lost out of every 100,000 packets. • If MSS is increased from 1500 bytes to 9000 bytes (Jumbo frames), limit on TCP BW will rise by a factor of 6.

  6. The Results

  7. Web100 Collaboration

  8. Parallel TCP Connections…a clue SOURCE: Harimath Sivakumar, Stuart Bailey, Robert L. Grossman. “PSockets: The Case for Application-level Network Striping for Data Intensive Applications using High Speed Wide Area Networks,” SC2000: High-Performance Network and Computing Conference, Dallas, TX, 11/00

  9. Why Does This Work? • Assumption is that network gives best effort throughput for each connection • But end-to-end performance is still poor, even after tuning the host, network, and application • Parallel Sockets are being used in GridFTP, Netscape, Gnutella, Atlas, Storage Resource Broker, etc.

  10. Packet Loss • Bolot* found that Random losses are not always due to congestion • local system configuration (txqueuelen in Linux) • Bad cables (noisy) • Packet losses occur in bursts • TCP throttles transmission rate on ALL packet losses, regardless of the root cause • Selective Acknowledgement (SACK) helps, but only so much * Jean-Chrysostome Bolot. “Characterizing End-to-End packet delay and loss in the Internet.”, Journal of High Speed Networks, 2(3):305--323, 1993.

  11. Expression for Parallel Socket Bandwidth

  12. Number of Connections Aggregate Bandwidth 1 100 50 Mb/sec 2 100+100 100 Mb/sec 3 100+100+100 150 Mb/sec 4 4 (100) 200 Mb/sec 5 5 (100) 250 Mb/sec Example MSS = 4418, RTT = 70 msec, p = 1/10000 for all connections

  13. Measurements • To validate theoretical model, 220 4 minute transmissions performed from U-M to NASA AMES in San Jose, CA • Bottleneck was OC-12, MTU=4418 • 7 runs MSS=4366, 1 to 20 sockets • 2 runs MSS=2948, 1 to 20 sockets • 2 runs MSS=1448, 1 to 20 sockets • Iperf used for transfer, Web100 used to collect TCP observations on sender side

  14. Actual: MSS 1448 Bytes

  15. Actual: MSS 2948 Bytes

  16. Actual: MSS 4366 Bytes

  17. Observations • Knee in the curve is the point at which aggregate throughput reaches the delay*bandwidth product of the pipe between sender and receiver. • On a long link (trans-Atlantic), the pipe can hold a lot of data, since the delay (RTT) is so large. • Parallel sockets should only work if there are are no router drops. TCP will try to fill the pipe on a single stream.

  18. Sunnyvale – Denver Abilene Link Initial Tests Yearly Statistics

  19. Abilene Weather Map

  20. Other stuff • Internet2 measurements on Cleveland Abilene node • Interesting results from ns-2 simulation – is it just a simulation artifact? • Working on a loss model for Abilene that differentiates between router drops and random drops

  21. Conclusion • High Performance Network Throughput is possible with a combination of host, network and application tuning along with using parallel TCP connections • Parallel TCP Sockets mitigate negative effects of packet loss in random congestion regime • Effects of Parallel TCP Sockets similar to using larger MSS • Using Parallel Sockets is aggressive, but as fair as using large MSS

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