Capturing Periodic I/O Using Frequency Techniques

TL;DR

This paper presents FTIO, an online Fourier-based method for detecting periodic I/O patterns in HPC applications, enabling improved scheduling and system utilization.

Contribution

Introduction of FTIO, a novel online Fourier transform-based technique for detecting I/O periodicity with confidence metrics and extensive validation.

Findings

FTIO achieves a mean error below 11%.

System utilization increased by 26% with FTIO.

I/O slowdown reduced by 56% using FTIO.

Abstract

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%.