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Adaptive Network Management Edwin Hernandez HCS - Lab  June 8th, 1999
Traffic characterization <ul><li>Hurst parameter provides information about the traffic type in the network </li></ul><ul>...
Measurements of Self-similarity in traffic patterns used To determine the hurst parameter, it is required to use the  Log(...
Self-similarity in traffic H=0.8137, TCP Traffic using as source Fractional Gaussian Noise tool (by Vern Paxon, et.al. UCB...
Results for Adaptive samplers Results using the previous policies. The problem is presented with H=0.5, where the STDev is...
Throughput with H=0.8 Systematic sampling at T=1s
Conclusions <ul><li>O(n) controllers does not work if N is increased and H=0.8, with the exception of O(2)  </li></ul><ul>...
Conclusion <ul><li>Some accuracy will be lost if we increase the granularity or sampling time. </li></ul><ul><li>Hurst par...
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Adaptive Sampling for Network Management - Fuzzy Logic

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Adaptive Sampling for Network Management, Fuzzy Logic

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Adaptive Sampling for Network Management - Fuzzy Logic

  1. 1. Adaptive Network Management Edwin Hernandez HCS - Lab June 8th, 1999
  2. 2. Traffic characterization <ul><li>Hurst parameter provides information about the traffic type in the network </li></ul><ul><li>Tests executed using H=0.5 and H=0.8 </li></ul><ul><li>Modification on sampling policies to increase accuracy in samples. </li></ul><ul><li>Tradeoff : Accuracy vrs Nr of Samples, discussed by Klaffy et.al and other papers </li></ul>
  3. 3. Measurements of Self-similarity in traffic patterns used To determine the hurst parameter, it is required to use the Log(Variance) vrs Log(granularity) graph. In this experiment the Granularity refers to different sampling times. This method is based in the property of slowly decaying variance. The relationship is defined by  =2H-2, and var(X (m) )  am -  , as m   H=0.5337, Videoconference data - multimedia traffic
  4. 4. Self-similarity in traffic H=0.8137, TCP Traffic using as source Fractional Gaussian Noise tool (by Vern Paxon, et.al. UCB). Traffic stimulation between hornet and raptor.
  5. 5. Results for Adaptive samplers Results using the previous policies. The problem is presented with H=0.5, where the STDev is 3.2 times the average value. High frequency components lost. With H=0.8, The STDev is only 0.31 of the average value.
  6. 6. Throughput with H=0.8 Systematic sampling at T=1s
  7. 7. Conclusions <ul><li>O(n) controllers does not work if N is increased and H=0.8, with the exception of O(2) </li></ul><ul><li>Substitute in 0.1*Tmax for a 10 seconds interval. </li></ul><ul><li>FLC works pretty well with H=0.8, but the decrease in samples is only 12% </li></ul><ul><li>O(n) and FLC performs similarly with H=0.5 or multimedia traffic </li></ul>
  8. 8. Conclusion <ul><li>Some accuracy will be lost if we increase the granularity or sampling time. </li></ul><ul><li>Hurst parameter not easy to calculate, requires time and a lot of samples. </li></ul>

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