Using machine learning methods to detect applications with abnormal efficiency

Abstract

At the moment a lot of supercomputing applications are inefficient in terms of the usage of available resources. To decrease the number of such inefficient applications, a tool for supercomputer task flow analysis and detection of inefficient application runs is needed. In this paper several supervised machine learning methods are considered to solve this issue. The classification performed by these methods is based on system monitoring data (e.g. CPU load, network usage etc.). The experiments on real data show that the Random Forest algorithm is currently the best option to accomplish given goal. At the moment the resulting classifier model is being tested on the “Lomonosov” supercomputer. The experiment results demonstrating the efficiency of the resulting model are also included in this paper.