Conditional clustered matrix factorization based network coordinate system

Network coordinate (NC) systems that use Euclidean distances suffer from the existence of Triangle Inequality Violations (TIVs). Matrix factorization (MF) based NC system is an alternative approach towards better prediction accuracy and can remove TIV. However, the prediction accuracy for short links in these systems still suffers from low prediction accuracy compared with the overall prediction accuracy. Two-layer systems have been proposed to improve the prediction accuracy for short links. They divide the whole space into several location-based clusters and run NC systems on both global layer and local layer. However, these systems do not improve the prediction accuracy for short links in the clusters with a few hosts. In this paper, the Conditional Clustered Network Coordinate (CCNC) system is proposed. It divides the space into a number of clusters in a balanced, dynamic, and decentralized way. In the proposed system, any node can join or disjoin the system without affecting the system accuracy. The performance of the CCNC system is evaluated with the King data set and the PlanetLab data set to be compared against two well known NC systems: Phoenix and Pancake. The simulation results show that CCNC outperforms Phoenix and Pancake significantly in terms of estimation accuracy, expected time to construct the clusters, and the communication overhead. Moreover, CCNC converges very fast and it is simple, scalable, dynamic, and robust under different dimension values.