A Study On P2P Streaming System With The Introduction Of Social Network

Widely used of P2P streaming technology and the rapid development of P2Pstreaming media technology complement each other. People need to build aneffective P2P streaming network coverage, better incentives overlay nodes within thenetwork actively participating in the system contribution, and prevention of thespread of malicious nodes in the system of pollution data block, thereby enhancingthe overall and ensure P2P streaming media system performance.Firstly, for the two current popular Internet applications, P2P streaming mediatechnology and social networks have been combined with the use of socialnetworking features to improve the overall performance of the user’s P2P streamingsystem. Based on the characteristics of virtual social networks, the concept of usingsocial intimacy to extract more closely connected with a truer friend node activityscreening using a stable social intimacy nodes constitute a social candidate neighbors.P2P streaming system in the process of building an overlay network, combined withsocial candidate neighbor nodes and random node neighbor list building. The use ofsocial relationships characterized by P2P streaming system compared to the fullrandom P2P streaming system, start-up delay, playback delay, jitter rate videoreceived a good income.Then, focus on how to improve the bandwidth utilization in P2P streamingsystem, stimulate the nodes to actively participate the streaming content distributionin the system and put forward the P2P streaming incentive mechanism based on the trust. First, the node with the current cycle of contribution, effective upload timeratio, the current live cycle time, the current available bandwidth and the trust valueof the last cycle, to compute the trust value. According to the trust value, theincentive value add or substract. Record the change of the incentive value change ina certain time interval, used to adjust the level of node position, different levels ofnode, get the different service quality.Finally, dynamic weights and dual-threshold trust management model.According to experience direct interaction between two nodes, dynamicallyadjusting the proportion of the trust value calculation direct trust value withthird-party recommendations, and give a more authentic relationship of trust betweenthe two nodes. And by setting a threshold of two-way trust value of nodes withdifferent properties were identified. Innocent node depending on network conditionshave led to a trust value buffer can connect in the next, again to improve theirconfidence in the value of, get rid of suspected malicious nodes. By reducing thenumber of malicious nodes innocent, try to ensure that the system under safeconditions, to obtain higher data transfer rates. Based on the trust value, the nodedata requests to the system only has a high trust value of nodes. In this way, the nodecan reduce the possibility of malicious nodes connected to them.