CC* Data Storage: Distributed, Fast, Scalable Infrastructure for Emerging Media Research Data

The project develops a fast, scalable, distributed cyberinfrastructure (CI) across New York University’s New York City buildings to meet the specialized demands of laboratory and classroom media researchers, including: 1) large-scale storage; 2) fast, low latency, two-way edge-server communications; and 3) scalable system design to rapidly adapt to emerging research frameworks, such as virtual/augmented reality (VR/AR), machine learning, and data science. The project architecture is based on research with colleagues operating the large Flatiron Institute Ceph clusters.

The CI fosters and supports data-driven, high concurrency, and collaborative research and educational activities. With VR/AR as an example use case, the CI generates a new service to provide an immediate enabling impact on real-time cloud-edge data transmission and storage, relaxing the computational load on low-power untethered headsets. The high concurrency data volume support enables remote virtual environment co-development by synchronously sharing data and models.

The strategic CI design supports other broad emerging research disciplines, including robotics, educational technologies, biomedical experimentation, and wireless networks. To broaden participation, the CI is accessible via the New York State Education and Research Network, as well as to other NYU collaborating K-12 educational institutions and government agencies.

The technology helps to support the training and education of students from low-income households and backgrounds underrepresented in STEM. Through the integration of ultra-large and instantaneous public and citizen data analysis, it also supports just-in-time data-driven urban policy-making and public emergency response.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.