J8.4 Unidata Cyberinfrastructure in the Cloud

Wednesday, 25 January 2017: 9:15 AM
611 (Washington State Convention Center )
Mohan K. Ramamurthy, UCAR, Boulder, CO

Data services, software, and user support are critical components of geosciences cyber-infrastructure to help researchers to advance science. With the maturity of and significant advances in cloud computing, it has recently emerged as an alternative new paradigm for developing and delivering a broad array of services over the Internet. Cloud computing is now mature enough in usability in many areas of science and education, bringing the benefits of virtualized and elastic remote services to infrastructure, software, computation, and data. Cloud environments reduce the amount of time and money spent to procure, install, and maintain new hardware and software, and reduce costs through resource pooling and shared infrastructure.

Given the enormous potential of cloud-based services, Unidata has been moving to augment its software, services, data delivery mechanisms to align with the cloud-computing paradigm. To realize the above vision, Unidata has worked toward:

* Providing access to many types of data from a cloud (e.g., via the THREDDS Data Server, RAMADDA and EDEX servers);

* Deploying data-proximate tools to easily process, analyze, and visualize those data in a cloud environment cloud for consumption by any one, by any device, from anywhere, at any time;

* Developing and providing a range of pre-configured and well-integrated tools and services that can be deployed by any university in their own private or public cloud settings. Specifically, Unidata has developed Docker for “containerized applications", making them easy to deploy. Docker helps to create “disposable” installs and eliminates many configuration challenges. Containerized applications include tools for data transport, access, analysis, and visualization: THREDDS Data Server, Integrated Data Viewer, GEMPAK, Local Data Manager, RAMADDA Data Server, and Python tools;

* Leveraging Jupyter as a central platform and hub with its powerful set of interlinking tools to connect interactively data servers, Python scientific libraries, scripts, and workflows;

* Exploring end-to-end modeling and prediction capabilities in the cloud;

* Partnering with NOAA and public cloud vendors (e.g., Amazon and OCC) on the NOAA Big Data Project to harness their capabilities and resources for the benefit of the academic community.

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