A Novel Passive-Landmark Based Network Distance Prediction Method

With the direction of network topology information, the performance of large scale distributed applications could be enhanced greatly. However, if the topology information between nodes is obtained by directly measure, the cost of the probing packets may be more than the gain from the performance improvement. This paper proposes a novel passive landmark based network distance prediction method-PLNDP. The vector of transmission delay from normal node to landmarks is embedded into the metric space R\+n by the Lipschitz transformation. After getting the network coordinates, normal nodes use the distance function to compute the distance between coordinates. Then the network distances between nodes is predicted by the distance between nodes coordinates. Unlike other network coordinates system, landmarks in PLNDP only need to respond to probes passively, while not measuring distances to other landmarks actively. Existing high performance public servers, such as DNS servers and Web servers, can be used as landmarks. So the cost of deployment can be reduced greatly. In order to improve the prediction accuracy, valid landmarks and correctional factor are used in the distance function. Experiment results show that, for several different accuracy metrics, PLNDP is better than classical network distance prediction methods GNP and Vivaldi, especially when some landmarks have been failed.