License Forecasting and Scheduling for HPC
Ahmed Burak Gulhan, Gulsum Gudukbay Akbulut, Amit Amritkar, Jack Sampson, Vasant Honovar, Adam Focht, Chuck Pavloski, Mahmut Kandemir · 2023
This work focuses on forecasting future license usage for high-performance computing environments and using such predictions to improve the effectiveness of job scheduling. Specifically, we propose a model that carries out both short-term and long-term license usage forecasting and a method of using forecasts to improve job scheduling. Our long-term forecasting model achieves a Mean Absolute Percentage Error (MAPE) as low as 0.26 for a 12-month forecast of daily peak license usage. Our job scheduling experimental results also indicate that wasted work from jobs with insufficient licenses can be reduced by up to 92% without increasing the average license-using job completion times, during periods of high license usage, with our proposed license-aware scheduler.