NDLI: Non-Euclidean Internet Coordinates Embedding

Content Provider	IEEE Xplore Digital Library
Author	Allan, A. Humphrey, R. Di Fatta, G.
Copyright Year	2013
Description	Author affiliation: Sch. of Syst. Eng., Univ. of Reading, Reading, UK (Allan, A.; Humphrey, R.; Di Fatta, G.)
Abstract	For many applications it is desirable to be able to estimate latency in a decentralised network when it is not practical to explicitly measure it. It has previously been shown that latency can be approximated by assigning hosts coordinates in some geometric space such that the Euclidean distance between two hosts in this space is equivalent to latency, a method known as a Network Coordinate (NC) system. This is commonly achieved by a large scale distributed optimisation which seeks to minimise the error between latency and Euclidean distance. In this work we challenge the assumption of Euclidean space as a satisfactory model for embedding Internet-like networks, due to the curved nature of network distances. We present a novel distributed optimisation methodology: Non-Euclidean Internet Coordinates Embedding (NICE). NICE uses a polynomial regression model to explicitly learn the most effective distance function for latency estimation within a geometric space, in addition to a distributed non linear dimensionality reduction method. Dimensionality reduction is achieved via a variant of Landmark Multi Dimensional Scaling (LMDS) and a distributed optimisation algorithm. This allows the distributed system to create a set of coordinates for each of the participating hosts that can be used to accurately estimate latency. The system is implemented within the Java based PeerSim network simulator using both real and artificially generated input topologies and then compared to two of the most widely implemented NC systems: GNP and Vivaldi. By experimental simulation we show that NICE is significantly more accurate than either method while still remaining robust in the face of real network conditions.
Sponsorship	Toshiba
Starting Page	295
Ending Page	302
File Size	311646
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Economic indicators Network topology Network Coordinates Multidimensional Scaling Euclidean distance Regression Polynomials Peer-to-peer computing Topology Non-Euclidean Distance
Content Type	Text
Resource Type	Article

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