Distributed computing systems are essential for meeting the demands of modern HPC applications, yet transitioning from single-system to distributed environments presents significant challenges. Resource misallocation in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. We evaluated BanditWare on two workflow applications: BurnPro3D (a web-based platform for fire science), and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently.

In recent years, frequent and highly destructive megafires become one of the biggest climate-induced disasters. Fire behavior models using data from many emerging sources can inform decision support tools to respond to and mitigate such megafires. Emerging edge sensing and computing technologies within the fire environment can enhance the speed, reliability, and efficiency of wildland fire management, leading to better prevention, faster response times, and more effective mitigation of fire-related disasters. However, a unified system that streamlines the integration of edge technology advances within fire science and management workflows is needed. This paper presents the design and demonstrated case studies of the WIFIRE Edge Platform that facilitates the integration of sensing and AI capabilities at the edge. The initial attack and prescribed burn concept scenarios are described, highlighting the sensor deployment and utilization at the fire front.

The FAIR and CARE data principles are critical to ensuring widespread and equitable access to open data. They provide guidelines for what should be contained in metadata and how certain types of data should be handled. This study examines how large, collective data hubs such as the WIFIRE Data Commons can implement the FAIR and CARE data principles through evaluating the datasets hosted on the platform. An automation pipeline was developed to check for specified criteria in these principles, allowing fast integration of the principles on a large scale data hub. This pipeline can be expanded to check for all FAIR and CARE criteria, and similar pipelines can be created for a variety of other data hubs. Automating for the FAIR and CARE principles will help simplify organization of open data, allowing for a greater expansion of open science.

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.