perfCorrelate: Performance variability correlation framework

Abstract

Edge computing is a promising technology for deploying time-sensitive and privacy-sensitive applications closer to the premises of users. However, it is crucial to identify the sources of performance variability caused by application co-location to meet user requirements effectively. Monitoring systems typically expose hundreds of metrics, making comprehensive analysis challenging. As a result, researchers often rely on a small, arbitrarily selected subset of metrics for tasks such as building performance predictors. In this paper, we examine how the available monitoring metrics are correlated with Round Trip Time (RTT) fluctuations and suggest directions for building performance models. Our experiments focus on a Single Particle Analysis (SPA) applications for an electron microscopy use case, deployed in a Kubernetes environment and monitored by Prometheus. We demonstrate that while a subset of monitoring metrics consistently correlates with performance, the specific metrics in this subset can vary due to dynamic application co-locations and observation windows. Consequently, the optimal number of metrics and the choice of machine learning model needed to accurately capture performance variability vary between different scenarios (co-location and cluster nodes). These differences directly impact the effectiveness of scheduling decisions in resource clusters, which depend on performance predictors. Our work presents a method to systematically identify the most relevant monitoring metrics to changes in RTT and determining the most representative observation window, ensuring a more generalizable understanding of the performance of the application throughout its lifecycle.