Mitigating Shared Storage Congestion Using Control Theory
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arXiv
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| Format: | Preprint |
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2025
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| author | Collignon, Thomas Halitim, Kouds Bleuse, Raphaël Cerf, Sophie Robu, Bogdan Rutten, Éric Seinturier, Lionel van Kempen, Alexandre |
| author_facet | Collignon, Thomas Halitim, Kouds Bleuse, Raphaël Cerf, Sophie Robu, Bogdan Rutten, Éric Seinturier, Lionel van Kempen, Alexandre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Shared Storage Congestion Using Control Theory Collignon, Thomas Halitim, Kouds Bleuse, Raphaël Cerf, Sophie Robu, Bogdan Rutten, Éric Seinturier, Lionel van Kempen, Alexandre Distributed, Parallel, and Cluster Computing Hardware Architecture 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. |
| title | Mitigating Shared Storage Congestion Using Control Theory |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture |
| url | https://arxiv.org/abs/2511.16177 |