Capturing Periodic I/O Using Frequency Techniques

Ahmad Tarraf (1) , Alexis Bandet (2, 3, 4) , Francieli Zanon Boito (3, 4) , Guillaume Pallez (5) , Felix Wolf (1)

1 TU Darmstadt - Technische Universität Darmstadt - Technical University of Darmstadt
2 UB - Université de Bordeaux
3 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique
5 KerData - Scalable Storage for Clouds and Beyond

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%.

Mots clés: HPC; I/O prediction; temporal I/O behavior; DFT

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Ahmad Tarraf, Alexis Bandet, Francieli Zanon Boito, Guillaume Pallez, Felix Wolf. Capturing Periodic I/O Using Frequency Techniques. IPDPS 2024 - 38th IEEE International Parallel & Distributed Processing Symposium, May 2024, San Francisco, United States. pp.1-13. ⟨hal-04382142⟩