Efficient and scalable data management becomes increasingly important in large-scale distributed storage systems. A key enabling technique is a flexible, balancing and scalable data object placement and location scheme that automatically adapts to the additions or departures of storage nodes. In this paper, a data object placement algorithm based on dynamic interval mapping is proposed, which is probabilistically optimal in both distributing data evenly and minimizing data movement when storage nodes is changed. Moreover, this algorithm supports weighted allocation of the storage nodes and variable levels of the object replication.