1 / 18

Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Hou

Outline. IntroductionPoisson modelSelf-Similar modelPoisson model vs. Self-Similar modelExperimental ResultCo-ExistenceRemarksReferences. Introduction. What is a model?Why do we need modeling?What are the kinds of models available?What are the models that I have discussed?. Poisson Model.

donar
Download Presentation

Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Hou

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Houston

    2. Outline Introduction Poisson model Self-Similar model Poisson model vs. Self-Similar model Experimental Result Co-Existence Remarks References

    3. Introduction What is a model? Why do we need modeling? What are the kinds of models available? What are the models that I have discussed? A model is anything that s used to describe a phenomenon or characteristics of an object. It is required in order to understand the behaviour of the phenomenon or object Models can be mathematical or analytical. Network traffic is represented by statistical models, namely Poisson model and Self-similar or fractal model A model is anything that s used to describe a phenomenon or characteristics of an object. It is required in order to understand the behaviour of the phenomenon or object Models can be mathematical or analytical. Network traffic is represented by statistical models, namely Poisson model and Self-similar or fractal model

    4. Poisson Model Poisson Process : Describes the number of times that some known event has occurred as a function of time, where events can occur at random times. Network traffic : Considered as a random arrival process under Poisson modeling. A Poisson process is a process that describes the number of times that some known event has occurred as a function of time, where events can occur at random times. The Poisson distribution gives the probability of observing n counts in a fixed time interval, when the expectation of the number of counts to be observed is L (lamda) A Poisson process is a process that describes the number of times that some known event has occurred as a function of time, where events can occur at random times. The Poisson distribution gives the probability of observing n counts in a fixed time interval, when the expectation of the number of counts to be observed is L (lamda)

    5. Packet arrival is considered as 1 or ON state and the inter arrival time is 0 or OFF state

    7. Self-Similar Model Self-Similarity: Something that feels the same irrespective of the scale. In case of stochastic objects like time-series, self-similarity is used in the distributional sense Long Range Dependence (LRD): The traffic is similar in longer spans of time.

More Related