Mitigating Shared Storage Congestion Using Control Theory
Year: 2025
Type: conference-paper
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.
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Lille, Centre Inria de l'Université de Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille, SPIRALS: Self-adaptation for distributed services and large software systems, Centrale Lille Institut, Laboratoire d'Informatique de Grenoble, CTRL-A: Commande pour systèmes informatiques autonomiques, Université Grenoble Alpes, Institut polytechnique de Grenoble, GIPSA-Lab
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416753087
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