NDLI: Embedding network coordinates into the heart of distributed hash tables

Content Provider	IEEE Xplore Digital Library
Author	Kojima, T. Asahara, M. Kono, K. Hayakawa, A.
Copyright Year	2009
Description	Author affiliation: Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan (Kojima, T.; Asahara, M.; Kono, K.; Hayakawa, A.)
Abstract	Network coordinates (NCs) construct a logical space which enables efficient and accurate estimation of network latency. Although many researchers have proposed NCbased strategies to reduce the lookup latency of distributed hash tables (DHTs), these strategies are limited in the improvement of the lookup latency; the nearest node to which a query should be forwarded is not always included in the consideration scope of a node. This is because conventional DHTs assign node IDs independent of the underlying physical network. In this paper, we propose an NC-based method of constructing a topology-aware DHT by Proximity Identifier Selection strategy (PIS/NC). PIS/NC assigns an ID to each node based on NC of the node. This paper presents Canary, a PIS/NC-based CAN whose d-dimensional logical space corresponds to that of Vivaldi. Our simulation results suggest that PIS/NC has the possibility of dramatically improving the lookup latency of DHTs. Whereas DHash++ is only able to reduce the median lookup latency by 15% of the original Chord, Canary reduces it by 70% of the original CAN.
Starting Page	155
Ending Page	158
File Size	370406
Page Count	4
File Format	PDF
ISBN	9781424450664
e-ISBN	9781424450671
DOI	10.1109/P2P.2009.5284517
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Delay Peer to peer computing Space technology Computer science Electronic mail Intrusion detection Network topology IP networks Euclidean distance
Content Type	Text
Resource Type	Article

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