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Performance Evaluation of PISA and PI using NS simulations

Performance Evaluation of PISA and PI using NS simulations. Presented by Brad Burres Yatin Manjrekar. Agenda. Introduction Background Setup Results Conclusion. Introduction . 80% of traffic flows are short (http) and represent 20% of data

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Performance Evaluation of PISA and PI using NS simulations

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  1. Performance Evaluation of PISA and PI using NS simulations Presented by Brad Burres Yatin Manjrekar

  2. Agenda • Introduction • Background • Setup • Results • Conclusion

  3. Introduction • 80% of traffic flows are short (http) and represent 20% of data • 20% of traffic flows are long and represent 80% of data • Prioritizing short flow transmission (dropping Long flows first) will help the network congestion • Our scope is limited to TCP.

  4. TCP Congestion control

  5. AQM congestion control • Droptail – FIFO • RED – Random Early Detection • SHRED- Short lived flow friendly RED • DCN-Differential Congestion Notification • PI – Proportional Integrator

  6. DCN Flow Chart

  7. PI algorithm

  8. PISA Algorithm • PISA – Proportional Integrator with Short-lived flow Adjustment • It clamps queue length to Qref • CWND hint in Type of Service field • Drop probability is increased or decreased depending on cwnd ratio

  9. PISA Algorithm cont.

  10. PISA Algorithm Cont Weighted Cwnd Average

  11. NS SETUP

  12. Simulation setup

  13. Simulations and Measurements Measurements Made • Queue Length • Instantaneous • Average • Drop Rate • Web Objects • Transmission Time • Items Transmitted • Utilization (pending)

  14. Queue Length (all graphs are the ftp 100, http 100, pareto = 1.3) PI AVG = 199.94 PISA AVG = 198.97 (both clamp to 200)

  15. Packets Dropped (ftp10,http100,1.3 VS. ftp100,http100,1.3)

  16. Web Object Transmission • PI • Started 28630 • Finished 28549 • PISA • Started 34358 • Finished 34273 • For both, tails go out to 500 seconds

  17. Conclusions • PISA does a better job at giving priority to short flows • There is still room for improvement • We still need to do more analysis of the data

  18. References • [UW] Stefan Saroiu, Krishna Gummadi, Richard Dunn, Steven Gribble, Henry Levy, “An Analysis of Internet Content Delivery Systems”. • [FJ93] S Floyd and V Jacobson, “Random Early Detection Gateways for Congestion avoidance”. IEEE/ACM Tractions on Networking • [CJO01] M Christiansen,K Jeffay, D. Ott and F.D.Smith, “Tuning RED for Web Traffic” IEEE/ACM Transactions on Networking. • [HCK02] M Hartling, M Claypool and R. Kinicki, “Active Queue Management for Web Traffic” • Technical Report WPI-CS-TR-02-20, Worcester Polytechnic Institute, May 2002 • [LAJS04] Long Le, Jay Aikat, Kevin Jeffay, F. Donelsom Smith “Differential Congestion Notification:Taming the elephant” IEEE/ICNP 04

  19. References Cont. • [K04]Minchong Kim, “Proportional Integrator with Short-lived flow adjustment” http://www.wpi.edu/Pubs/ETD/Available/etd-0122104-154529/unrestricted/mjkim.pdf • Thesis submitted to WPI Faculty, Jan 2004 • [S04] David Sonderling. “Master Qualifying Project”. MQP submitted to WPI Faculty. 2004. • [NS201]NS-2 Network Simulator http://www.isi.edu.nsnam/ns, September 2001 • Jae Chung and Mark Claypool “NS by example” http://nile.wpi.edu/NS/ • http://www.freesoft.org/CIE/Course/Section3/7.htm

  20. Q & A ?? Comments

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