Improvements in distribution of meteorological data using application layer multicast

Abstract

The Unidata Program Center is an organization working with the University Center for Atmospheric Research (UCAR), in Colorado. It provides a broad variety of meteorological data, which is used by researchers in many real-world applications. This data is obtained from observation stations and distributed to various universities worldwide, using Unidata’s own Internet Data Distribution (IDD) system, and software called the Local Data Manager (LDM). The existing solution for data distribution has many limitations, like high end-toend latency of data delivery, increased bandwidth usage at some nodes, poor scalability for future needs and manual intervention for adjusting to changes or faults in the network topology. Since the data is used in so many applications, the impact of these limitations is often substantial. This thesis removes these limitations by suggesting improvements in the IDD system and the LDM. We present new algorithms for constructing an application-layer data distribution network. This distribution network will form the basis of the improved LDM and the IDD system, and will remove most of the limitations given above. Finally, we perform simulations and show that our algorithms achieve better average end-to-end latency as compared to that of the existing solution. We also compare the performance of our algorithms with a randomized solution. We find that for smaller topologies (where the number of nodes in the system are less than 38) the randomized solution constructs efficient distribution networks. However, if the number of nodes in the system increases (more than 38), our solution constructs efficient distribution networks than the randomized solution. We also evaluate the performance of our algorithms as the number of nodes in the system increases and as the number of faults in the system increases. We find that even if the number of faults in the system increases, the average end-to-end latency decreases, thus showing that the distribution topology does not become inefficient.