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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.08601 |
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| _version_ | 1866914872910938112 |
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| author | Tarraf, Ahmad Bandet, Alexis Boito, Francieli Pallez, Guillaume Wolf, Felix |
| author_facet | Tarraf, Ahmad Bandet, Alexis Boito, Francieli Pallez, Guillaume Wolf, Felix |
| contents | Many HPC applications perform their I/O in bursts that follow a periodic pattern. This allows for making predictions as to when a burst occurs. System providers can take advantage of such knowledge to reduce file-system contention by actively scheduling I/O bandwidth. The effectiveness of this approach, however, depends on the ability to detect and quantify the periodicity of I/O patterns online. In this paper, we introduce FTIO, an online method to detect periodic I/O phases, which is based on discrete Fourier transform (DFT), combined with outlier detection. We provide metrics that gauge the confidence in the output and tell how far from being periodic the signal is. We validate our approach with large-scale experiments on a production system and examine its limitations extensively. Our experiments show that FTIO has a mean error below 11%. Finally, we demonstrate that FTIO allowed the I/O scheduler Set- 10 to boost system utilization by 26% and reduce I/O slowdown by 56%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_08601 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Capturing Periodic I/O Using Frequency Techniques Tarraf, Ahmad Bandet, Alexis Boito, Francieli Pallez, Guillaume Wolf, Felix Distributed, Parallel, and Cluster Computing Many HPC applications perform their I/O in bursts that follow a periodic pattern. This allows for making predictions as to when a burst occurs. System providers can take advantage of such knowledge to reduce file-system contention by actively scheduling I/O bandwidth. The effectiveness of this approach, however, depends on the ability to detect and quantify the periodicity of I/O patterns online. In this paper, we introduce FTIO, an online method to detect periodic I/O phases, which is based on discrete Fourier transform (DFT), combined with outlier detection. We provide metrics that gauge the confidence in the output and tell how far from being periodic the signal is. We validate our approach with large-scale experiments on a production system and examine its limitations extensively. Our experiments show that FTIO has a mean error below 11%. Finally, we demonstrate that FTIO allowed the I/O scheduler Set- 10 to boost system utilization by 26% and reduce I/O slowdown by 56%. |
| title | Capturing Periodic I/O Using Frequency Techniques |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2306.08601 |