Research On P2P Storage System Based On CAN

With the growth of storage applications which based on P2P network and the increasing scale of P2P storage network, the hot spot in recent years falls on how to make a reasonable use of the limited web resource. This dissertation will solve the problem from the improved CAN algorithm, data distribution and the data recovery strategy.At first, in order to reduce the hoop count and increase search efficiency, the notion of pointer table is introduced into CAN. In a scale of 2L identifier space, coordinates in each dimension are divided with Binary Search. The nodes which correspond with the divided coordinates compose the next hoop set as a pointer table which cuts down the search scope from whole CAN to a local CAN area. Simulation results show that the distribution of nodes'coordiniates generated by improved CAN search algorithm is more uniform than that of the original CAN model. In scale of 26 CAN and scale of 27 CAN, 90% and 70% searches cut their hoop figures, and the rates of decrease are 53.2% and 31.5% respectively. The distribution rates of search length reduction in the cases of scale of 25, 25 and 27 CANs are given after the sample space is extent. The experiment demonstrates that the algorithm of improved CAN has less search hoop count than that of the original.Secondly, this dissertation establishes a P2P network node model by analyzing the rule of online nodes, with which, the online time, online probability and the online length have been proposed. Above this foundation, the Inspiring type, supplementary type and random type distribution strategy in traditional P2P storage system are improved. A p2p storage system has been established using the above improved strategies. The storage system has less data retrieving under a certain guarantee of data usability and system reliability, so as to reduce the expenses caused by the data recovery.At last, the performance of strategies has been tested by simulation experiment, showing the usability of the system in reducing the network expenses.