Workload Similarity Analysis using Machine Learning Techniques

Abstract

Finding the similarity between two workload behaviors is helpful in 1. creating proxy workloads 2. characterizing an unknown workload's behavior by matching its behavior against known workloads. In this article, we propose a method to measure the similarity between two workloads using machine learning-based analysis of the performance telemetry data collected for the execution runs of the two workloads. We also demonstrate the accuracy of the technique by measuring the similarity between a variety of know benchmark workloads.