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In this paper, we refine previous work on a model for a Distributed Hash Table (DHT) with support to dynamic balancement across a set of heterogeneous cluster nodes. We present new high-level entities, invariants and algorithms developed to increase the level of parallelism and globally reduce memory utilization. In opposition to a global distribution mechanism, that relies on complete knowledge about the current distribution of the hash table, we adopt a local approach, based on the division of the DHT into separated regions, that possess only partial knowledge of the global hash table. Simulation results confirm the hypothesis that the increasing of parallelism has as counterpart the degradation of the quality of the balancement achieved with the global approach. However, when compared with Consistent Hashing and our global approach, the same results clarify the relative merits of the extension, showing that, when properly parameterized, the model is still competitive, both in terms of the quality of the distribution and scalability.