We compare structured and unstructured overlays and derive a hybrid overlay that can outperform both. Unstructured overlays build a random graph and use flooding or random walks on that graph to discover data stored by overlay nodes. Structured overlays assign keys to data items and build a graph that maps each key to the node that stores the corresponding data. Unstructured overlays are widely used in popular applications because they can perform complex queries more efficiently than structured overlays. It is also commonly believed that structured graphs are more expensive to maintain than unstructured graphs and that the constraints imposed by the structure make it harder to exploit heterogeneity to improve scalability. This is not a fundamental problem. We describe techniques that exploit structure to achieve low maintenance overhead, and we present a modified proximity neighbor selection algorithm that can exploit heterogeneity effectively. We performed detailed comparisons of structured and unstructured graphs using simulations driven by real-world traces. Inspired by these results, we developed a hybrid system that uses the graph from structured overlays with the data placement and search strategies of unstructured overlays. The results show that our hybrid system supports complex queries more efficiently than unstructured overlays in realistic scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"
We compare structured and unstructured overlays and derive a hybrid overlay that can outperform both. Unstructured overlays build a random graph and use flooding or random walks on that graph to discover data stored by overlay nodes. 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