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Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks

Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks. Presented By:. Frank Posluszny. Vishal Phirke. Matt Hartling. Outline. Background. Network Power. Simulation Topology. Weakness of RED - Motivation. Algorithm. Simulations & Comparisons RED Vs READ.

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Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks

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  1. Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks Presented By: Frank Posluszny Vishal Phirke Matt Hartling

  2. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  3. Background (1) Goals: • Show drawbacks of RED with ECN • Propose new AQM: Random Early Adaptive Detection

  4. Background (2) • TCP congestion control • Congestion Control vs. Avoidance • RED • ECN

  5. Background (3) ECN: • Binary feedback scheme • Router sets a bit in packet to “mark” instead of drop • ACK mirrors the marking back to receiver

  6. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  7. What’s Power?? Throughput Delay Throughput optimized N/W -Great throughput- Takes 15minutes to view a web page. Delay optimized N/W -Low Delays – But the web page is missing a lot of information.…. Throughput Response Time Power =

  8. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  9. Simulation Topology Bottleneck Queue Size = 60 pkts Pkt Size = 512 bytes MINth= 15 MAXth= 45

  10. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  11. Weakness of RED - Motivation

  12. Weakness of RED - Motivation

  13. 10 flows Weakness of RED - Motivation

  14. 60 flows Weakness of RED - Motivation

  15. Weakness of RED - Motivation 20 flows

  16. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  17. Random Early Adaptive Detection Exponentially Weighted Moving Averages Avgt+1 = (1-wq) avgt + wq qt Old weighted average Instantaneous queue Slt+1 = (1-wsl) slt + wsl (avgt+1 – avgt) Old weighted slope Instantaneous slope

  18. Random Early Adaptive Detection At each change of MIN (MAX + MIN) 2 level = if(level > buffer * 0.52) p = p + INC INC = 0.02 if(level < buffer * 0.48) DEC = 0.002 p = p - DEC

  19. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  20. Fig 5: Throughput Vs. Delay

  21. READ Vs. RED (1) RED: • Lower Drop probability = Higher Throughput & Higher Delay • Higher Drop probability = Lower Delay & Lower Throughput READ: • Always Lower Delay and Higher Throughput

  22. Fig 6: Power (alpha=1)

  23. Fig 7: Power (alpha = 2)

  24. READ Vs. RED (2) RED: • Performance varies with maxp and number of flows • Performs worse than Drop Tail under certain conditions READ: • Always performs better than RED and Drop Tail

  25. Table 1: Throughput For Mixed Traffic

  26. Fig 8 & 9: Adaptation to Changes in Network Conditions

  27. READ Vs. RED (3) RED: • Large variation in instantaneous and average queue size • Large variation in marking probability • Marking probability varies with queue size READ: • Less variation in marking probability and queue size • Large, periodic fluctuations

  28. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  29. Fig 10: READ Tuning

  30. Outline • Background • Network Power • Simulation Topology • Weakness of RED - Motivation • Algorithm • Simulations & Comparisons RED Vs READ • READ Tuning • Conclusions and Future Work

  31. Conclusions and Future Work Conclusions: • RED can fail & too aggressive • READ – reliable CA; higher power levels Current & Future Work: • Examine different increase/decrease algorithms • READ with different Network Topologies

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