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Maximizing End-to-End Network Performance

This paper explores the ways to maximize end-to-end network performance through bandwidth reservation and performance tuning. It discusses the issues with poor network performance and provides solutions to improve actual throughput using performance tuning. The use of parallel TCP connections is also introduced as a method to mitigate the negative effects of packet loss.

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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 5, 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. Sunnyvale – Denver Abilene Link Initial Tests Yearly Statistics

  18. Abilene Weather Map

  19. 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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