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Eng Keong Lua

Researcher at Monash University

Publications -  44
Citations -  2559

Eng Keong Lua is an academic researcher from Monash University. The author has contributed to research in topics: Overlay network & The Internet. The author has an hindex of 14, co-authored 44 publications receiving 2487 citations. Previous affiliations of Eng Keong Lua include Nanyang Polytechnic & Monash University, Clayton campus.

Papers
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Journal ArticleDOI

A survey and comparison of peer-to-peer overlay network schemes

TL;DR: A survey and comparison of various Structured and Unstructured P2P overlay networks is presented, categorize the various schemes into these two groups in the design spectrum, and discusses the application-level network performance of each group.
Book

P2P Networking and Applications

TL;DR: P2P networking has recently emerged as a viable multimillion dollar business model for the distribution of academic and clinical information, telecommunications, and social networking, and Peer-to-Peer Networking and Applications explains the conceptual operations and architecture underlying basic P2P systems using well-known commercial systems as models.
Book ChapterDOI

Internet routing policies and round-trip-times

TL;DR: This paper investigates how routing policies for both intra- and inter-domain routing can naturally give rise to violations of the triangle inequality with respect to RTTs, and argues that TIVs should not be treated as measurement artifacts, but rather as natural features of the Internet's structure.
Proceedings ArticleDOI

On the accuracy of embeddings for internet coordinate systems

TL;DR: This work defines several new accuracy metrics that attempt to quantify various aspects of user-oriented quality in Internet coordinate systems and indicates that their quality is not as high as that suggested by the use of absolute relative error.
Journal ArticleDOI

Phoenix: A Weight-Based Network Coordinate System Using Matrix Factorization

TL;DR: This paper proposes an NC system, so-called Phoenix, which is based on the matrix factorization model, and shows that Phoenix achieves a scalable yet accurate end-to-end distances monitoring and is able to characterize TIV better than other existing NC systems.