Massive content distribution on overlay networks stresses both the server and the network resources because of the large volumes of data, relatively high bandwidth requirement, and many concurrent clients. While the server limitation can be circumvented by replicating the data at more nodes, the network limitation is far less easy to cope with, due to the difficulty in determining the cause and location of congestion and in provisioning extra resources. In this paper, we present novel schemes

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... r massive content distribution, that assign the clients to appropriate servers, so that the network load is reduced and also well balanced, and the network resource consumption is low. Our schemes allow scaling to very large system because the algorithms are very efficient and do not require network measurement, or topology or routing information. The core problems are formulated as partitioning the clients into disjoint subsets according to the degree of interference criterion, which reflects network resource usage and the interference among the concurrent connections. We prove that these problems are NPcomplete, and present heuristic algorithms for them. Through simulation, we show that the algorithms are simple yet effective in achieving the design goals.