Accurate Performance Predictors for Edge Computing Applications

2025-10-23

AI Summary

In dynamic edge computing, inaccurate performance prediction arises from application co-location and node heterogeneity. Method: This paper proposes a lightweight, customized performance prediction framework for real-time scheduling. It automatically identifies critical performance metrics from historical monitoring data and jointly optimizes multiple machine learning models to achieve Pareto-optimality between prediction accuracy and inference latency. Furthermore, it customizes model selection per server’s heterogeneous characteristics to adapt to dynamic coexistence scenarios. Contribution/Results: Experimental evaluation demonstrates up to 90% prediction accuracy and inference latency below 1% of end-to-end round-trip time (RTT), significantly improving resource scheduling efficiency and system predictability for electron microscopy workflows in edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Predicting application performance in resource-constrained edge environments
• Addressing performance unpredictability from co-location and node heterogeneity
• Optimizing accuracy and inference time for efficient performance predictors

Innovation

• Automatically builds and assesses performance predictors
• Prioritizes accuracy and inference time for efficiency
• Uses correlated monitoring metrics for predictor training