Network distance prediction for enabling service-oriented applications over large-scale networks

Knowledge of end-to-end network distances is essential to many service-oriented applications such as distributed content delivery and overlay network multicast, in which the clients have the flexibility to select their servers from among a set of available ones based on network distance. However, due to the high cost of global measurements in large-scale networks, it is infeasible to actively probe end-to-end network distances for all pairs. In order to address this issue, network distance prediction has been proposed by measuring a few pairs and then predicting the other ones without direct measurements, or splicing the path segments between each pair via observation. It is considered important to improve network performance, and enables service-oriented applications over large-scale networks. In this article, we first illustrate the basic ideas behind network distance prediction, and then categorize the current research work based on different criteria. We illustrate how different protocols work, and discuss their merits and drawbacks. Finally, we summarize our findings, and point out potential issues and future directions for further research.