Geoscience Cyberinfrastructure in the Cloud: Data-Proximate Computing to Address Big Data and Open Science Challenges

Abstract

Data are not only the lifeblood of the geosciences but they have become the currency of the modern world both in science and in society. Rapid advances in computing, communications, and observational technologies – along with concomitant advances in high-resolution modeling, ensemble and coupled-systems predictions of the Earth system – are revolutionizing nearly every aspect of the geosciences. Modern data volumes from high-resolution ensemble prediction systems and next-generation remote-sensing systems like hyper-spectral satellite sensors and phased-array radars are staggering. The advent and maturity of cloud computing technologies and tools have opened new avenues for addressing both big data and Open Science challenges to accelerate scientific discoveries. There is broad consensus that as data volumes grow rapidly, it is particularly important to reduce data movement and bring processing and computations to the data. Data providers also need to give scientists an ecosystem that includes data, tools, workflows and other end-to-end applications and services needed to perform analysis, integration, interpretation, and synthesis - all in the same environment or platform. Instead of moving data to processing systems near users, as is the tradition, one will need to bring processing, computing, analysis and visualization to data - so called data proximate workbench capabilities, also known as server-side processing.