Towards a Methodology for Building Dynamic Urgent Applications on Continuum Computing Platforms

Abstract

Advanced cyberinfrastructure aims at making the use of streaming data a common practice in the scientific community. They offer an ecosystem that links data, compute, network, and users to deliver knowledge obtained from multiple data sources using large-scale computational models. However, integrating this heterogeneous data with time-sensitive systems is difficult due to a lack of programming abstractions that can allow data-driven reactive behaviors throughout the edge-to-cloud/HPC computing continuum. Here we present a methodology for incorporating contextual information into the application logic while taking into consideration the heterogeneity of the underlying platform and the unpredictability of the data. A fire science scenario that includes sensors at the network's edge for smoke detection and computational models launched in the cloud for wildfire simulation and air quality assessment serves as the inspiration for this method. We then discuss research directions for tackling similar scenarios with a particular focus on resource management and programming models.