A Distributed Local Clustering Method for Large-Scale Resource Discovery
In large-scale resource environments,traditional resource indexing mechanisms lead to a rapid increase in the number of Peer nodes and a decrease in load balancing performance,affecting query efficiency and system stability.This paper introduces a centroid model-based local resource clustering method,which clusters similar resources at a single node and selects a representative key value,effectively reducing the scale of Peer nodes in the peer-to-peer(P2P)network.Additionally,the local clustering mechanism focuses on processing closely related key values,thus preventing excessive expansion of resource coverage.Experimental results demonstrate that the Skip Graph algorithm based on the centroid model not only reduces query complexity and improves load balancing performance,but also exhibits excellent scalability in terms of network size,data volume,and query complexity,better adapting to the needs of large-scale resource discovery.
local clusteringresource discoverypeer-to-peer(P2P)networkcentroid model
万方数据
维普
