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arXiv · 2511.16177

Mitigating Shared Storage Congestion Using Control Theory

Thomas Collignon·Kouds Halitim·Raphaël Bleuse·Sophie Cerf·Bogdan Robu·Éric Rutten·Lionel Seinturier·Alexandre van Kempen
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Abstract

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.

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Thomas Collignon, Kouds Halitim, Raphaël Bleuse, Sophie Cerf, Bogdan Robu, Éric Rutten, Lionel Seinturier, Alexandre van Kempen. 2025-11-20. Mitigating Shared Storage Congestion Using Control Theory. https://doi.org/10.1145/3773274.3774277

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