Using Machine Learning Techniques to Classify the Interference of HPC applications in Virtual Machines with Uncertain Data

Abstract

This work aims to predict the level of interference caused by concurrent access to shared resources, such as cache and main memory, that can drastically affect the performance of HPC applications executed in clouds, by using some well-known machine learning techniques. As the user does not know the exact number of resource accesses in practice, we propose a human-readable categorization of these accesses. The used dataset contains information about synthetic and real HPC applications, and, to reflect the uncertainty of the user categorization, we inserted some noisy data in it. Our results showed that our approach could correctly predict the level of interference in most cases, indicating that it can be a practical solution.