Harnessing the Computing Continuum for Urgent Science

Abstract

Urgent science describes time-critical, data-driven scientific work-flows that can leverage distributed data sources in a timely way to facilitate important decision making. While our capacity for generating data is expanding dramatically, our ability to manage, analyze, and transform this data into knowledge in a timely manner has not kept pace. This paper explores how the computing continuum, spanning resources at the edges, in the core, and in-between, can be harnessed to support urgent science and discusses associated research challenges. Using an Early Earthquake Warning (EEW) workflow, which combines data streams from geo-distributed seismometers and high-precision GPS stations to detect large ground motions, as a driver, we propose a system stack that can enable the fluid integration of distributed analytics across a dynamic infrastructure spanning the computing continuum.