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Maximizing End-to-End Network Performance Thomas Hacker University of Michigan October 5, 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

MaximizingEnd-to-End Network Performance

Thomas Hacker

University of Michigan

October 5, 2001

introduction
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
improve network performance
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
tcp bandwidth limits mathis equation
TCP Bandwidth Limits – Mathis Equation
  • Based on characteristics from physical layer up to transport layer.
  • Hard Limits
  • TCP Bandwidth, Max Packet Loss
packet loss and mss
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.
parallel tcp connections a clue
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

why does this work
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.
packet loss
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.

example

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

measurements
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
sunnyvale denver abilene link
Sunnyvale – Denver Abilene Link

Initial Tests

Yearly Statistics

conclusion
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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