Cloud computing offers key benefits of scalability, cost, and redundancy compared with traditional data centers. However most of the tools and software systems developed for NASA data repositories…

This federal contract award, valued at $743,504.47, was granted to The HDF Group, a non-profit organization, by the NASA Shared Services Center. The contract is for the development of a web service called Highly Scalable Data Service (HSDS) that will use object storage to provide access to NASA's Earth science data, taking advantage of the scalability and cost-effectiveness of cloud computing. The goal is to enable compatibility with existing tools and applications by creating client libraries API-compatible with HDF5 and NetCDF4. The contract is a two-year Cooperative Agreement that runs from July 2016 to July 2020. There is no set-aside designation for this award.

Cloud computing offers key benefits of scalability, cost, and redundancy compared with traditional data centers. However most of the tools and software systems developed for NASA data repositories were not developed with the cloud in mind and do not fully take advantage of cloud-based technologies. To address this, we propose the development of a web service (highly scalable data service or hsds) that will use object storage rather than file based storage to vend earth science data. Object storage services are provided through all the leading public (amazon AWS, microsoft azure, google cloud, etc.) and private clouds (open stack), and provide the most cost-effective means of storing large data collections online. In addition to being cost-effective, the service will be highly scalable in that IT will be able to support an effectively unlimited number of clients at high throughput. To enable compatibility with existing tools and applications, we will create client libraries that are API compatible with existing libraries for HDF5 and NETCDF4. We will demonstrate the capabilities of this service by setting up a platform on amazon AWS that integrates the service with existing NEX/opennex datasets and tools.