Analytics of Longitudinal System Monitoring Data for Performance Prediction
This work addresses performance optimization for HPC facilities, but it is incremental as it builds on existing monitoring and modeling approaches.
The paper tackled the problem of predicting job performance in HPC systems using longitudinal monitoring data, resulting in models that identify key predictors and can generalize to unseen applications.
In recent years, several HPC facilities have started continuous monitoring of their systems and jobs to collect performance-related data for understanding performance and operational efficiency. Such data can be used to optimize the performance of individual jobs and the overall system by creating data-driven models that can predict the performance of jobs waiting in the scheduler queue. In this paper, we model the performance of representative control jobs using longitudinal system-wide monitoring data and machine learning to explore the causes of performance variability. We analyze these prediction models in great detail to identify the features that are dominant predictors of performance. We demonstrate that such models can be application-agnostic and can be used for predicting performance of applications that are not included in training.