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Recent work has demonstrated that path diversity is an effective way to improve the end-to-end performance of network applications. For every node pair in a full-mesh network with n nodes, this paper presents a family of new approaches that efficiently identify an acceptable indirect path that has a similar to or even better performance than the direct path, hence considerably scaling the network at the cost of low per-node traffic overhead. In prior techniques, every node frequently incurs O(n1.5) traffic overhead to probe the links from itself to all other nodes and to broadcast its probing results to a small set of nodes. In contrast, in our approaches, each node measures its links to only O(√n) other nodes and transmits the measuring results to O(√n) other nodes, where the two node sets of size O(√n) are determined by the partial sampling schemes presented in this paper. Mathematical analyses and trace-driven simulations show that our approaches dramatically reduce the per-node traffic overhead to O(n) while maintaining an acceptable backup path for each node pair with high probability. More precisely, our approaches, which are based on enhanced and rotational partial sampling schemes, are capable of increasing said probability to about 65 and 85 percent, respectively. For many network applications, this is sufficiently high such that the increased scalability outweighs such a drawback. In addition, it is not desirable to identify an outstanding backup path for every node pair in reality, due to the variable link quality.