Research On Optimization Of P2P Real-time Streaming Media Management Protocol

The rapid development of the Internet promotes the P2P (Peer to Peer) streaming media technology forward.Now, China is one of the fastest growing countries in the development of P2P streaming media system. Because the nodes in the P2P streaming systems are anonymous and self-governing, the node’s target is to maximize their own utility, at the same time minimize the contribution to other nodes. A large number of nodes tend to only download not upload (free-riding). Due to the openness of the P2P network, the P2P streaming media system is also facing a number of security issues,such as,some nodes spread fake messages and malicious resources, collusion between nodes. With the expansion of the scale of P2P streaming media system,the number of the media resources grows rapidly,so it is difficult for users to find interesting media. These phenomena seriously reduce the performance of the P2P streaming media system, The existence of free-riding nodes even leads to the complete paralysis of network. So it is very necessary to design a management protocol to manage nodes and analysis user’s behavior.There are many researches about these three aspects at home and abroad, but most of these are based on P2P networks, not combining streaming media technology; and most of the researches are only unilateral studies,not applying incentive mechanism, trust mechanism,personalized recommender system to P2P real-time streaming media system.This paper is based on P2P streaming media system,on this basis, designing management protocol based on trust mechanism, incentive mechanism, personalized recommender system.Firstly,this paper describes the background and significance of this topic, the main work and objective of the thesis, the relevant knowledge of P2P streaming media system, analyzing the requirements of this system,giving the interface between management protocol and transport protocols,the interface between management protocol and signaling protocol.Secondly, Analyzing the free-riding problem that exists in the system, optimizing the incentive mechanism based on game theory, according to the actual situation of the P2P streaming media system, taking the node’s stability and upload bandwidth costs into account when calculating the node’s contribution value.Through the comparison with other incentive mechanisms,the paper concludes that the incentive mechanism based on the game theory has advantages in promoting nodes involved in resources and information sharing.Thirdly, In the case of system security issues,this paper optimizes the trust mechanism based on reputation, the mechanism obtains the target node’s comprehensive trust according to the node’s direct trust and indirect trust.before calculating the target node’s indirect trust, this mechanism first filters malicious nodes and collusion nodes in recommend node set. Finally,Designing a personalized recommender system for users which is based on user clustering and collaborative filtering and recommends programs to users that they may interested in.The paper develops the experimental simulation platform to test the management protocol, analyzes and summaries the experimental results.