Compact, Adaptive Placement Schemes for Non-Uniform Capacities

In this paper we study the problem of designing compact, adaptive strategies for the distribution of objects among a heterogeneous set of servers. Ideally, such a strategy should allow the computation of the position of an object with a low time and space complexity, and it should be able to adapt with a near-minimum amount of replacements of objects to changes in the capabilities of the servers so that objects are always distributed among the servers according to their capabilities. Previous techniques are able to handle these requirements only in part. For example, standard hashing techniques can be used to achieve a non-uniform distribution of objects among a set of servers and the time and space efficient computation of the position of the objects, but they usually do not adapt well to a change in the capabilities. We present two strategies based on hashing that achieve all of the goals above. Furthermore, we give a list of applications for these strategies demonstrating that they can be used efficiently for distributed data management, web caches, and adaptive random graphs, which may be of interest for peer-to-peer networks.