Towards network triangle inequality violation aware distributed systems
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
On the accuracy of decentralized virtual coordinate systems in adversarial networks
Proceedings of the 14th ACM conference on Computer and communications security
Distributed algorithms for stable and secure network coordinates
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
On the internet delay space dimensionality
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Phoenix: Towards an Accurate, Practical and Decentralized Network Coordinate System
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Practical connectivity-based routing in wireless sensor networks using dimension reduction
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
HNDP: a novel network distance prediction mechanism
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
Predicting available bandwidth of internet path with ultra metric space-based approaches
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Robust Decentralized Virtual Coordinate Systems in Adversarial Environments
ACM Transactions on Information and System Security (TISSEC)
Taming the triangle inequality violations with network coordinate system on real internet
Proceedings of the Re-Architecting the Internet Workshop
Network coordinates in the wild
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Multi-manifold model of the Internet delay space
Journal of Network and Computer Applications
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Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.