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A Model Approach to the Estimation ofPeer to Peer Traffic Matrices
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A Model Approach to the Estimation ofPeer - to - Peer Traffic Matrices

Category : Networking Project


Sub Category : JAVA


Project Code : ITJNW10


Project Abstract

A Model Approach to the Estimation of

Peer-to-Peer Traffic Matrices

ABSTRACT

 

In this paper, we propose a model to estimate P2P traffic matrices in operational networks. Important factors are considered, including the number of peers, the localization ratio of P2P traffic, and the network distance. Here, the distance can be measured with AS hop counts or geographic distance. To validate our model, we evaluate its performance using traffic traces collected from both the real P2P video-on-demand and file-sharing applications. Evaluation results show that the proposed model outperforms the other two typical models for the estimation of the general traffic matrices in several metrics, including spatial and temporal estimation errors, stability in the cases of oscillating and dynamic flows, and estimation bias.

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

However, existing approaches cannot be directly applied to the estimation of P2P traffic matrices.

 

Because the high elasticity feature of P2P traffic makes these approaches suffer from either unrealistic assumptions or high measurement costs.

 PROPOSED CONCEPT:-

We propose a model to estimate P2P traffic matrices based on a close analysis of the traffic characteristics in P2P systems.

Firstly, the number of peers is considered because, intuitively, networks with more peers might have larger volumes of P2P traffic.

EXISTING TECHNIQUE:-

Inference and Modeling

PROPOSED TECHNIQUE:-

The Aggregation of P2P Traffic Matrices

TECHNIQUE DEFINITION:-

Bayesian Inference probability theory is a branch of mathematical probability theory that allows one to model uncertainty about the world and outcomes of interest by combining common-sense knowledge and observational evidence.

TECHNIQUE DEFINITION:-

To capture the critical properties of the P2P traffic, we take the following physically meaningful factors into consideration. Factor is the traffic localization ratio, which covers the internally Exchanged portion of P2P traffic.

 

DRAWBACKS:-

We cannot derive an overall picture of traffic interaction by exploring user behaviors.

It cannot reduce the load on the Internet, and achieve shorter downloading time.

We not evaluate the estimation accuracy.

ADVANTAGES:-

We can derive an overall picture of traffic interaction by exploring user behaviors.

Reduce the load on the Internet, and achieve shorter downloading time.

We also evaluate the estimation accuracy of our model.

 
 
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