LSH-based distributed similarity indexing with load balancing in high-dimensional space

Locality-sensitive hashing (LSH) and its variants are well-known indexing schemes for solving the similarity search problem in high-dimensional space. Traditionally, these indexing schemes are centrally managed and multiple hash tables are needed to guarantee the search quality. However, due to the limitation of storage space and processing capacity of the server, the centralized indexing schemes become impractical for massive data objects. Therefore, several distributed indexing schemes based on peer-to-peer (P2P) networks are proposed, whereas how to ensure load balancing is still one of the key issues. To solve the problem, in this paper, we propose two theoretical LSH-based data distribution models in P2P networks for datasets with homogeneous and heterogeneous \(l_2\) norms, respectively. Unlike earlier schemes, to our knowledge, we focus on load balancing for a single hash table rather than multiple tables, which has not been considered previously. Then, we propose a static distributed indexing scheme with a novel load balancing indexing mapping method based on the cumulative distribution function by our models. Furthermore, we propose a dynamic load rebalancing algorithm using virtual node method of P2P networks to make the static indexing scheme more practical and robust. The experiments based on synthetic and real datasets show that the proposed distributed similarity indexing schemes are effective and efficient for load balancing in similarity indexing of high-dimensional space.