Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System

Cloud storage system poses new challenges to the community to support efficient concurrent querying tasks for various data-intensive applications, where indices always hold important positions. In this paper, we explore a practical method to construct a two-layer indexing scheme for multi-dimensional data in diverse server-centric cloud storage system. We first propose RT-HCN, an indexing scheme integrating R-tree based indexing structure and HCN-based routing protocol. RT-HCN organizes storage and compute nodes into an HCN overlay, one of the newly proposed sever-centric data center topologies. Based on the properties of HCN, we design a specific index mapping technique to maintain layered global indices and corresponding query processing algorithms to support efficient query tasks. Then, we expand the idea of RT-HCN onto another server-centric data center topology DCell, discovering a potential generalized and feasible way of deploying two-layer indexing schemes on other server-centric networks. Furthermore, we prove theoretically that RT-HCN is both space-efficient and query-efficient, by which each node actually maintains a tolerable number of global indices while high concurrent queries can be processed within accepted overhead. We finally conduct targeted experiments on Amazon's EC2 platforms, comparing our design with RT-CAN, a similar indexing scheme for traditional P2P network. The results validate the query efficiency, especially the speedup of point query of RT-HCN, depicting its potential applicability in future data centers.