Accurate Performance Predictors for Edge Computing Applications

TL;DR

This paper introduces a systematic methodology for building accurate, low-latency performance predictors tailored for resource-constrained edge environments, enhancing scheduling and resource management for diverse applications like electron microscopy workflows.

Contribution

It presents an automatic approach to develop and evaluate performance predictors that optimize both accuracy and inference time in dynamic, heterogeneous edge settings.

Findings

Predictors achieve up to 90% accuracy

Inference time is less than 1% of Round Trip Time

Systematic predictor selection improves resource utilization

Abstract

Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains challenging due to the co-location of multiple applications and the node heterogeneity. To address this, we propose a methodology that automatically builds and assesses various performance predictors. This approach prioritizes both accuracy and inference time to identify the most efficient model. Our predictors achieve up to 90% accuracy while maintaining an inference time of less than 1% of the Round Trip Time. These predictors are trained on the historical state of the most correlated monitoring metrics to application performance and evaluated across multiple servers in dynamic co-location scenarios. As usecase we consider electron microscopy (EM) workflows, which have stringent real-time demands and diverse resource requirements. Our findings emphasize the need for a systematic methodology that selects server-specific predictors by jointly optimizing accuracy and inference latency in dynamic co-location scenarios. Integrating such predictors into edge environments can improve resource utilization and result in predictable performance.