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Packet implementation: discretization

Packet implementation: discretization. Rates Control:. or. Packet implementation: discretization. ECN based price control:. Link:. Source:. Shift-register to save the last N ECN bits. Equilibrium:. Window dynamics:. Packet implementation: Source Side.

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Packet implementation: discretization

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  1. Packet implementation: discretization Rates Control: or

  2. Packet implementation: discretization ECN based price control: Link: Source: Shift-register to save the last N ECN bits Equilibrium: Window dynamics:

  3. Packet implementation: Source Side

  4. Packet implementation: Link Side

  5. Ns Simulation: two-way, long-lived traffic 32 ftps 32 ftps td1 td1 32 ftps Duplex links 32 ftps td2 td2 2Gbps, delay=20ms 64ftps td3 td3 64ftps td4 td4 128 ftps 128 ftps td5 td5 256 ftps 256 ftps td_i=(i-1)*10ms Scenario: 2^6, 2^6, 2^7,2^8,2^9 sources started at 0, 20, 40, 60, 80 seconds RTTs 40, 80,120,160, 200ms,link capacity 2Gbps (250pkts/ms)

  6. Ns Simulation: two-way, long-lived traffic New Protocol Cwnds Rates (pkts/sec) Queue (pkts) Utilization Marking Prob Estimated Prob

  7. Ns Simulation: two-way, long-lived traffic NewReno/RED NewReno/AdaptiveRED Cwnds Cwnds Queue (pkts) Queue (pkts) Utilization Utilization Paremeters: Thresh_ 100, maxthresh_ 2500

  8. Ns Simulation: two-way, long-lived traffic NewReno/VQ NewReno/PI Cwnds Cwnds Queue (pkts) Queue (pkts) Utilization Utilization Paremeters: qref_=100

  9. Ns Simulation: small marking Prob New Protocol Cwnds Rates (pkts/sec) Queue (pkts) Utilization Marking Prob Estimated Prob

  10. Ns Simulation: “heavy-tailed” traffic Long/heavy-tailed distributions Power law Crovella data set, 36208 files Pareto law lognormal: log X normally distr., (q,s,m)

  11. Ns Simulation: “heavy-tailed” traffic Pareto(scale,shape): Count contribution of the flows Ex: Pareto(100,1.0) Log2(Prob[X>x]) Prob(X<x) 34078 flows 3.33e+7 pkts Median size 200.5pkts flow size(pkts, log2(x)) flow size(pkts, log2(x)) packet contribution of the flows flow size(pkts, log2(x)) flow size(pkts, log2(x))

  12. Ns Simulation: “heavy-tailed” traffic New Protocol Cwnds Percentage of the sessions Queue Marking Prob Percentage of the packets Utilization Flow size (log2(x)) Cumulative Distribution of the flows Scenario: 1024 sources started at [0,10] with RTT 100ms, link capacity 1Gbps (125pkts/ms)

  13. Ns Simulation: “heavy-tailed” traffic New Protocol Cwnds Percentage of the sessions Queue (pkts) Percentage of the packets Marking Prob Utilization Flow size (log2(x)) Scenario: 2^6, 2^6, 2^7,2^8,2^9 sources started uniformly in [0,10] secs with RTTs 40, 80, 120,160, 200ms,link capacity 1Gbps (125pkts/ms) Cumulative Distribution of the flows

  14. Ns Simulation: “heavy-tailed” traffic NewReno/RED NewReno/AdaptiveRED Cwnds Cwnds Queue (pkts) Queue (pkts) Utilization Utilization Paremeters: Thresh_ 100, maxthresh_ 2500

  15. Ns Simulation: “heavy-tailed” traffic NewReno/PI NewReno/VQ Cwnds Cwnds Queue (pkts) Queue (pkts) Utilization Utilization

  16. Packet implementation: tricks ? Window management Price estimation: Pacing output Capping the change of the cwnd Penalizing the real queue above a threshold Smoothing the average arrival rate of the queue

  17. Conclusion: Equation-based implementation has the desirable performance. --scalable stable, fair, high utilization, small queue --especially for the high bandwidth links Price feedback and estimation is the main obstacle. Improvement may be made to be more efficient. To be done... -- packet-drop and timeout -- optimal parameter set -- new price estimation and transmission scheme -- more practical implementation

  18. Packet implementation: Source Side

  19. Ns Simulation: “heavy-tail” traffic

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