# Accurate Performance Predictors for Edge Computing Applications

arXiv: https://arxiv.org/abs/2510.20495 · HTML: https://mlsanity.com/paper/2510.20495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [A reconfigurable smart camera implementation for jet flames characterization based on an optimized segmentation model](https://mlsanity.com/paper/2604.03267/) (2026)

## Abstract (arXiv)

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.