ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems

TL;DR

This paper introduces PrismIO, a Python tool that analyzes I/O traces and uses machine learning to predict optimal storage sub-systems, including burst buffers, for large-scale applications, achieving over 94% accuracy.

Contribution

The work presents a novel machine learning approach combined with a Python-based analysis tool to predict suitable storage configurations for I/O-intensive applications.

Findings

Model achieves 94.47% accuracy on unseen IOR scenarios.

Model achieves 95.86% accuracy on four real applications.

PrismIO effectively identifies I/O bottlenecks and performance issues.

Abstract

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application's I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify bottlenecks on burst buffers and parallel file systems and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and burst buffer selections. We run IOR (an I/O benchmark) with various I/O characteristics on different storage systems and collect performance data. We use the data as the input for training the model. Our model can predict if a file of an application should be placed on BBs for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%.