Efficient data access in High-Performance Computing (HPC) systems is essential to the performance of intensive computing tasks. Traditional optimizations of the I/O stack aim to improve peak performance but are often workload specific and require deep expertise, making them difficult to generalize or re-use. In shared HPC environments, resource congestion can lead to unpredictable performance, causing slowdowns and timeouts. To address these challenges, we propose a self-adaptive approach based on Control Theory to dynamically regulate client-side I/O rates. Our approach leverages a small set of runtime system load metrics to reduce congestion and enhance performance stability. We implement a controller in a multi-node cluster and evaluate it on a real testbed under a representative workload. Experimental results demonstrate that our method effectively mitigates I/O congestion, reducing total runtime by up to 20% and lowering tail latency, while maintaining stable performance.
@article{arxiv.2511.16177,
title = {Mitigating Shared Storage Congestion Using Control Theory},
author = {Thomas Collignon and Kouds Halitim and Raphaël Bleuse and Sophie Cerf and Bogdan Robu and Éric Rutten and Lionel Seinturier and Alexandre van Kempen},
journal= {arXiv preprint arXiv:2511.16177},
year = {2025}
}