2. Abstract Self-similar processes based on fractal point processes (FPP) provide natural and attractive network traffic models. FPP models provide useful in evaluating and predicting the queuing performance of various types of fractal traffic sources.

3. Introduction Various stochastic models and techniques have beeen proposed for modeling the distinct statistical nature of self-similar network traffic. EX Brown motion,Markovian models

4. Introduction Four FPP 1.fractal renewal process(FRP) 2.superposition of several fractal renewal processes (Sup-FRP) 3.fractal-shot –noise driven poisson process (FSNDP) 4.fractal binomial noise driven process (FBNDP)

5. Provide two methods which yield parsimonious flexible FPP One based on renewal processes and the other on doubly stochatic poisson processes.

6. FPP Fractals are objects whitch process a form of self-similarity parts of the shole can be made to fit to the whole in some way by scaling.

7. Seocond –order statistcal measures PSD:power spectrum density CR:coincidence rate IDC:index of disperation for counts(fano factor) COV:count-based covariance dunction.

9. λ= E{N[T]}/T is expected rate

10. 2.3Fractal nature of FPPs

13. 3.Two FPP construction method 3.1 Renewal point process method 3.2 Doubly stochastic poison process