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LDHT: Locality-aware Distributed Hash Tables

Authors: Weiyu Wu; Yang Chen 0001; Xinyi Zhang; Xiaohui Shi; Lin Cong; Beixing Deng; Xing Li 0001;

LDHT: Locality-aware Distributed Hash Tables

Abstract

As the substrate of structured peer-to-peer systems, distributed hash table (DHT) plays a key role in P2P routing infrastructures. Traditional DHT does not consider the location of the nodes for the assignment of identifiers, which will result in high end-to-end latency on DHT-based overlay networks. In this paper, we propose a design of locality-aware DHT called LDHT, which exploits network locality on DHT-based systems. Instead of assigning uniform random node identifiers in traditional DHT, nodes in LDHT are assigned locality-aware identifiers according to their autonomous system numbers (ASNs). As a result, each node will have more nearby neighbors than faraway neighbors in the overlay. We evaluate the performance of LDHT on different kinds of typical DHT protocols and on various topologies. The results show that LDHT improves the traditional DHT protocols a lot in terms of end-to-end latency, without introducing additional overhead. It is indicated that LDHT is fit for different kinds of DHT protocols and can work effectively on all structured P2P systems including Chord, Symphony and Kademlia.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Average
Top 10%
Top 10%
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