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An Analysis of AIMD Algorithm with Decreasing Increases

An Analysis of AIMD Algorithm with Decreasing Increases. Yunhong Gu , Xinwei Hong, and Robert L. Grossman. National Center for Data Mining. Outline. TCP’s inefficiency in grid applications Improvements on AIMD AIMD with decreasing increases (DAIMD) The UDT algorithm Experimental result

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An Analysis of AIMD Algorithm with Decreasing Increases

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  1. An Analysis of AIMD Algorithm with Decreasing Increases Yunhong Gu, Xinwei Hong, and Robert L. Grossman National Center for Data Mining

  2. Outline • TCP’s inefficiency in grid applications • Improvements on AIMD • AIMD with decreasing increases (DAIMD) • The UDT algorithm • Experimental result • Conclusion and future work

  3. TCP and AIMD • AIMD (Additive Increase Multiplicative Decrease) • Fair: max-min fairness • Stable: globally asynchronously stable • But, inefficient and not scalable • In grid networks (with high bandwidth-delay product)

  4. Efficiency of TCP 1 Gb/s link, 200ms RTT, between Tokyo and Chicago 28 minutes On 10 Gb/s link, 200ms RTT, it will take 4 hours 43 minutes to recover from a single loss. TCP’s throughput model: It needs extremely low loss rate on high bandwidth-delay product networks.

  5. Improvements of TCP • Fixed parameter (e.g., 1 segment per RTT) is not scalable and hence inefficient • 32 segments per RTT works fine for 1 Gb/s link, but how about its performance on 40Gb/s link or 1.5Mb/s link? • Increasing the increase parameter as the congestion window increases • E.g., Scalable TCP and HighSpeed TCP • Cause fairness and convergence problem

  6. AIMD with Decreasing Increases • To reach high efficiency, the increase parameter of an AIMD-based algorithm should be correlated to the link capacity and the available bandwidth. • XCP uses available bandwidth and number of concurrent flows to calculate next increase parameter • The increase parameter should be large at the beginning and decreases as the sending rate increases.

  7. AIMD with Decreasing Increases

  8. UDT - UDP based Transport Protocol • Application level built above UDP • End-to-end approach • Rate based control • The sending rate is tuned per constant interval (SYN).

  9. UDT Algorithm • UDT considers end-to-end link capacity L • It is hard to estimate the number of concurrent flows and real-time available bandwidth • UDT tunes the increase parameter according to L-C, where C is the current sending rate.

  10. UDT Algorithm (1) (2) (3) (4) (5)

  11. UDT Algorithm L = 10 Gbps, S = 1500 bytes

  12. UDT Algorithm

  13. UDT: Efficiency and Fairness Characteristics • Takes 7.5 seconds to reach 90% of the link capacity, independent of BDP • Satisfies max-min fairness if all the flows have the same end-to-end link capacity • Otherwise, any flow will obtain at least half of its fair share • Does not take more bandwidth than concurrent TCP flow as long as

  14. Experiment - Setup

  15. Experiment - Results

  16. Conclusion • Standard TCP is inefficient for grid applications in high bandwidth-delay product networks. • We argued that the increase parameter should be correlated to such information as link capacity and available bandwidth. • We analyzed a class of AIMD based control algorithm whose increase parameter is decreasing as the sending rate increases and proved that it is fair and stable. • According to this analysis we designed a new control algorithm that uses estimated link capacity and the current sending rate as the hints to update increase parameter. • This algorithm has been implemented in our UDT protocol and the experiments have demonstrated very good performance.

  17. Future Work • Bandwidth Estimation • Currently UDT uses packet pairs to estimate link capacity • We will consider more methods to deal with cross traffic and NIC interrupt coalescence

  18. Thank you! Questions and comments are welcome! For more information, please visit http://www.ncdm.uic.edu http://udt.sf.net

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