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Dynamic Flow Modelling

Dynamic Flow Modelling. represent traffic distribution in a network by mean of flows a flow is an intermediate entity between packet and the intrinsic switching granularities it can model the traffic send by an application (voice, video, file transfer, HTTP,…)

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Dynamic Flow Modelling

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  1. Dynamic Flow Modelling • represent traffic distribution in a network by mean of flows • a flow is an intermediate entity between packet and the intrinsic switching granularities • it can model the traffic send by an application (voice, video, file transfer, HTTP,…) • it can model an aggregation of micro-flows (LAN output,…) • it can be specified by a set of parameters, modeling traffic behavior (duration, average throughput, burstiness,…-) • BUT the behavior at the packet level is implicit and NOT simulated > several order of magnitude less events beginning of the flow Representative parameters l, s,CoS Distributions for arrival, duration, destination end of the flow

  2. Main inputs • Dynamic traffic matrix • Flows characterized by their source/destination, mean rate, burstiness and mean time on • Time evolution of the probability of flow arrivals for long term variations of average traffic • Three-dimension Traffic Matrix Modeling of short / long term traffic variations Time • Flexible Traffic - Class granularities • Service class (eg. voice call, Web data, …) • Aggregated flow (eg. Enterprise LAN, VPN, …) • Fine grain (eg. Optical switching granularity, …) Space Source - Destination Traffic Class

  3. Source type Access Rate (b/s) Mean Rate (b/s) Burstiness Mean Time (min) Voice_classic 64000 64000 1 7 Voice_compressed 8000 4000 2 5 Web_modem 56000 3000 18,66666667 45 Web_DSL 1000000 50000 20 45 Data_DS3 45000000 5000000 9 15 Data_Geth 1000000000 100000000 10 10 Data_SAN 1000000000 1000000000 1 5 Flow characterization PDF Server profile Residential profile Service profile

  4. Minute scale Second scale Example of the Flow activity at various time scale Hour scale

  5. Traffic profile modeling for network dimensioning studies • Method: approximation of the equivalent Bandwidth of aggregated data flows • Reference: “Equivalent Capacity and its application to Bandwidth allocation in high-speed packet networks”, R. Guerin et al. (IBM research division), IEEE JSAC, vol 9, No 7, Sept 1991. • Goal: evaluate the stat. mux gain obtained in the aggregation of several data flows, with respect to the sum of these individual flows • Equivalent Bandwidth formulae: C= m + a.s, where • m is the mean traffic value of the aggregated flow and s its standard deviation • a = [-2.ln -ln(2)], where e is the neglected part in the trail of the probability density function of the aggregated flow (assumption that not all individual sources are not emitting at the same time). The lower is e (typically 10-10), more cases are taken into account, the more severe is the aggregation. Example: for N individual identical flows (with mel and sel are resp. the = mean rate and the standard deviation of an elementary source) C= Smel + a.  [Ssel2]

  6. Traffic modeling from Guerin approach • Currently to characterize the flows, we use • the Burstiness (ITU meaning) =Peak rate/Mean rate • the mean rate • For mainly flows, we assume : Peak rate = Access rate • Our expertise : • Gaussian approximation not valid with too bursty source. • When average traffic from clients aggregation is lower (or order of) than one active client then the Gaussian approximation is not valid. • Approximation not valid with flow with long packet burst duration. • Evolution of our model: • Replace the burstiness. • Enhance the Guerin formula on the Gaussian approximation.

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