Dynamo: a transparent dynamic optimization system
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
A scalable content-addressable network
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Indexing Techniques for Advanced Database Systems
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Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems
Middleware '01 Proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms Heidelberg
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P-Grid: a self-organizing structured P2P system
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P-ring: an efficient and robust P2P range index structure
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Map-reduce-merge: simplified relational data processing on large clusters
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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Providing scalable database services is an essential requirement for extending many existing applications of the Cloud platform. Due to the diversity of applications, database services on the Cloud must support large-scale data analytical jobs and high concurrent OLTP queries. Most existing work focuses on some specific type of applications. To provide an integrated framework, we are designing a new system, epiC, as our solution to next-generation database systems. In epiC, indexes play an important role in improving overall performance. Different types of indexes are built to provide efficient query processing for different applications. In this paper, we propose RT-CAN, a multi-dimensional indexing scheme in epiC. RT-CAN integrates CAN [23] based routing protocol and the R-tree based indexing scheme to support efficient multi-dimensional query processing in a Cloud system. RT-CAN organizes storage and compute nodes into an overlay structure based on an extended CAN protocol. In our proposal, we make a simple assumption that each compute node uses an R-tree like indexing structure to index the data that are locally stored. We propose a query-conscious cost model that selects beneficial local R-tree nodes for publishing. By keeping the number of persistently connected nodes small and maintaining a global multi-dimensional search index, we can locate the compute nodes that may contain the answer with a few hops, making the scheme scalable in terms of data volume and number of compute nodes. Experiments on Amazon's EC2 show that our proposed routing protocol and indexing scheme are robust, efficient and scalable.