Semi-Experiments and the Modelling of Internet Traffic - SAMSI

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Uniform In-Flow Arrivals [A-Pois; P-Uni] 20 18 16 0.016 0.062 0.25 1 4 16 64 256 1024 Data [A−Pois] [A−Pois; P−Uni] log 2 Variance( j ) 14 12 10 8 6 4 −6 −4 −2 0 2 4 6 8 10 scale j 25
What have we learned so far ? Flow Arrival Manipulation • Correlation between flows can be neglected • For IP modelling, flow arrivals can be modelled as Poisson Packets Within Flow Manipulation • LRD not caused by arrival process <strong>of</strong> packets within flows • Small scale behaviour governed by structure within flows • Source <strong>of</strong> structure (scaling ?) at small scales identified 26
Page 1 and 2: Semi-Experiments and the Modelling
Page 3 and 4: Study Second Order Structure, Using
Page 5 and 6: Biscaling 20 0.016 0.062 0.25 1 4 1
Page 7 and 8: Periodogram vs Wavelet Spectrum 20
Page 9 and 10: Similar ‘Biscaling’ in both Y a
Page 11 and 12: Empirical Data IP flow: set of pack
Page 13 and 14: Extracting Flow Information IP flow
Page 15 and 16: Original TCP Data Time 14
Page 17 and 18: Permutation of Flows [A-Perm] DATA
Page 19 and 20: Original Order Poisson Arrivals [A-
Page 21 and 22: Original Order Poisson Arrivals [A-
Page 23 and 24: Permuted Poisson Arrivals [A-Pois]
Page 25: Uniform In-Flow Arrivals [A-Pois; P
Page 29 and 30: Rejection of Large Flows [A-Pois; P
Page 31 and 32: Flow Arrival Manipulation Semi-Expe
Page 33 and 34: Properties of X(t), and Model Featu
Page 35 and 36: Modelling Flows: Averages over Flow
Page 37 and 38: Use Gamma Inter-arrivals for A Semi
Page 39 and 40: Modelling a Typical Auckland IV Tra
Page 41 and 42: The Body of P Impacts Medium Scales
Page 43 and 44: The Center of Gravity has Shifted F
Page 45 and 46: Using the Model for Scenario Explor

Semi-Experiments and the Modelling of Internet Traffic - SAMSI

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