Work

The arctic knowledge-based system: Science gateway integration for petascale arctic data processing and geospatial feature prediction

Year: 2026

Type: article

Abstract: Science gateways have become essential platforms that integrate computational resources, data services, and workflows for domain researchers, enabling artificial intelligence-driven (AI) analyses at scale. Building on this paradigm, we introduce the Science Gateway component of the Arctic Knowledge-Based System (A-KBS), designed to advance AI-assisted modeling of permafrost dynamics and other Arctic geospatial processes. The A-KBS provides researchers with a unified portal to configure and execute multi-horizon prediction tools for active layer thickness, ground deformation, wildfire occurrence, freeze/thaw states, soil and air temperature analyses, and to run global scale geospatial HPC workflows leveraging data from across the circumpolar Arctic. This system orchestrates workloads through Kubernetes-based (K8s) containerized environments, Globus Data Transfer/Compute services, and distributed computing tools such as Slurm, ParSL, and Ray.io. Its web portal is deployed on the University of North Dakota’s (UND) virtualized, load-balanced K8s cluster with cloud migration enabled by Rancher, while Python-based AI functions authored in JupyterHub are executed on remote systems through Globus Compute Endpoints. Current development has integrated the A-KBS with the UND high-performance computing (HPC) Talon cluster and Amazon Web Services-managed K8s resources, with a roadmap in place to extend this integration to other HPC environments including the San Diego Supercomputer Center’s Expanse System. By coupling scalable infrastructure with a suite of existing AI-driven workflows for environmental prediction tasks, the A-KBS accelerates Arctic science, strengthens cryospheric research, and supports decision-making through its integration with cyberinfrastructure surrounding the DRP (Defense Resiliency Platform Against Extreme Cold Weather) initiative. • The Arctic Knowledge-Based System (A-KBS) Science Gateway is introduced for AI-assisted Arctic research. • Deployment on a Kubernetes-based architecture validated the platform’s stability, scalability, and interoperability. • Integration of Globus and JupyterHub streamlined remote computing and eliminated user-side infrastructure complexity. • Unified authentication, secure data management, and automated monitoring ensured reliable operation for Arctic workflows.

Source: Applied Computing and Geosciences

DOI: 10.1016/j.acags.2026.100322