NEST: Locality-aware Approximate Query Service for Cloud Computing

Cloud computing applications face the challenges of dealing with a huge volume of data that needs the support of fast approximate queries to enhance system scalability and improve quality of service, especially when users are not aware of exact query inputs. Locality-Sensitive Hashing (LSH) can support the approximate queries that unfortunately suffer from imbalanced load and space inefficiency among distributed data servers, which severely limits the query accuracy and incurs long query latency between users and cloud servers. In this paper, we propose a novel scheme, called NEST, which offers ease-of-use and cost-effective approximate query service for cloud computing. The novelty of NEST is to leverage cuckoo-driven locality-sensitive hashing to find similar items that are further placed closely to obtain load-balancing buckets in hash tables. NEST hence carries out flat and manageable addressing in adjacent buckets, and obtains constant-scale query complexity even in the worst case. The benefits of NEST include the increments of space utilization and fast query response. Theoretical analysis and extensive experiments in a large-scale cloud testbed demonstrate the salient properties of NEST to meet the needs of approximate query service in cloud computing environments.