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Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
An Adaptive Protocol for Efficient Support of Range Queries in DHT-Based Systems
ICNP '04 Proceedings of the 12th IEEE International Conference on Network Protocols
OpenDHT: a public DHT service and its uses
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
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Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
BATON: a balanced tree structure for peer-to-peer networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ATEC '04 Proceedings of the annual conference on USENIX Annual Technical Conference
Beehive: O(1)lookup performance for power-law query distributions in peer-to-peer overlays
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Beehive: O(1)lookup performance for power-law query distributions in peer-to-peer overlays
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
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NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Brushwood: distributed trees in peer-to-peer systems
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A modular framework for the development of peer-to-peer applications and services
International Journal of Grid and Utility Computing
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Range queries, retrieving all keys within a given range, is an important add-on for Distributed Hash Tables (DHTs), as they rely only on exact key matching lookup. In this paper we support range queries through a balanced tree algorithm, Decentralized Balanced Tree, that runs over any DHT system. Our algorithm is based on the B+-tree design that efficiently stores clustered data while maintaining a balanced load on hosts. The internal structure of the balanced tree is suited for range queries operations over many data distributions since it easily handles clustered data without losing performance. We analyzed, and evaluated our algorithm under a simulated environment, to show it's operation scalability for both insertions and queries. We will show that the system design imposes a fixed penalty over the DHT access cost, and thus inherits the scalability properties of the chosen underlying DHT.