Supporting climate research using named data networking
Climate and other big data applications face substantial problems in terms of data storage, retrieval, sharing and management. While several community repositories and tools are available to help with climate data, these problems still persist and the community is actively looking for better solutions. In this project we apply NDN to support climate modeling applications. The information-centric nature of NDN, where content becomes a first class entity, simplifies many of the problems in this domain. NDN offers lightweight data publication, discovery and retrieval compared to IP-based solutions. However, introducing a new network architecture to a mature domain that routinely produces petabytes of datasets and a plethora of assorted tools to manipulate them, is a risky proposition. The advantages of NDN alone may not be sufficient to overcome the natural inertia. Our approach is to introduce NDN while carefully avoiding undue disruption to existing workflows. To that extent we employ a user interface that employs familiar filesystem operations to publish, discover and retrieve data, integrated with domain-specific translators that automatically convert and publish datasets as NDN objects. We outline the advantages of NDN in this application domain and the challenges we faced during the adaptation. We believe this is the first exercise in applying NDN in an existing, large, mature application domain.
2014 IEEE 20th International Workshop on Local and Metropolitan Area Networks, LANMAN 2014
Olschanowsky, C., Shannigrahi, S., & Papadopoulos, C. (2014). Supporting climate research using named data networking. 2014 IEEE 20th International Workshop on Local and Metropolitan Area Networks, LANMAN 2014 https://doi.org/10.1109/LANMAN.2014.7028640