Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System
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Cloud storage system poses new challenges to the community to support efficient concurrent querying tasks for various data-intensive applications, where indexes 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. RT-HCN is proposed, which is 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 indexes 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 indexes while high concurrent queries can be processed within accepted overhead.