RDIM: A Self-adaptive and Balanced Distribution for Replicated Data in Scalable Storage Clusters

As storage systems scale from a few storage nodes to hundreds or thousands, data distribution and load balancing become increasingly important. We present a novel decentralized algorithm, RDIM (Replication Under Dynamic Interval Mapping), which maps replicated objects to a scalable collection of storage nodes. RDIM distributes objects to nodes evenly, redistributing as few objects as possible when new nodes are added or existing nodes are removed to preserve this balanced distribution. It supports weighted allocation and guarantees that replicas of a particular object are not placed on the same node. Its time complexity and storage requirements compare favorably with known methods.