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This work presents a framework for estimating job wait times in High-Performance Computing (HPC) scheduling queues, leverag- ing historical job scheduling data and real-time system metrics. Using machine learning techniques, specifically Random Forest and Multi-Layer Perceptron (MLP) models, we demonstrate high accuracy in predicting wait times, achieving 94.2% reliability within a 10-minute error margin. The framework incorporates key fea- tures such as requested resources, queue occupancy, and system utilization, with ablation studies revealing the significance of these features. Additionally, the framework offers users wait time esti- mates for different resource configurations, enabling them to select optimal resources, reduce delays, and accelerate computational workloads. Our approach provides valuable insights for both users and administrators to optimize job scheduling, contributing to more efficient resource management and faster time to scientific results. 

ACCESS is a program established and funded by the National Sci- ence Foundation to help researchers and educators use the NSF na- tional advanced computing systems and services. Here we present an analysis of the usage of ACCESS allocated cyberinfrastructure over the first 16 months of the ACCESS program, September 2022 through December 2023. For historical context, we include analyses of ACCESS and XSEDE, its NSF funded predecessor, for the ten-year period from January 2014 through December 2023. The analyses in- clude batch compute resource usage, cloud resource usage, science gateways, allocations, and users.