Accelerate Data Retrieval by Multi-Dimensional Indexing in Switch-Centric Data Centers. (20th December 2018) - Luo, Xinjian; Gao, Xiaofeng; Chen, Guihai
Abstract: Data centers, receiving increased attention in data management and analysis communities, have posed new challenges in data-intensive applications, among which efficient querying processing holds a critical position. To accelerate the efficiency of multi-dimensional data retrieval, we propose a distributed multi-dimensional indexing scheme for switch-centric data centers in this paper. We first propose FR-Index, a two-layer indexing system integrating both Fat-tree topology and R-tree indexing structure. In the lower layer, each server indexes the local data with R-tree, while in the upper layer the distributed global index depicting an overview of the whole dataset. Based on the Fat-tree topology, we design a specific indexing space partitioning and mapping strategy for efficient global index maintenance and query processing. Furthermore, we develop a cost model to dynamically update FR-Index. Experiments on Amazon's EC2 platform, comparing FR-Index with RT-CAN and RB-Index, show that the proposed indexing schema is scalable, efficient and lightweight, which can significantly promote the efficiency of query processing in data centers.