Performance modeling of heterogeneous edge-cloud systems with machine learning
Uddin, Md Raihan Asaduzzaman, Abu Gowda, Sonu Gangadhar
Uddin, Md Raihan
Asaduzzaman, Abu
Gowda, Sonu Gangadhar
Citations
Altmetric:
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2025-10-16
Type
Conference paper
Genre
Keywords
Radio frequency,Training,Adaptation models,Energy consumption,Accuracy,Recurrent neural networks,Computational modeling,Predictive models,Throughput,Long short term memory
Subjects (LCSH)
Citation
Uddin, M. R., Asaduzzaman, A., & Gowda, S. G. (2025, September 15). Performance modeling of heterogeneous edge-cloud systems with machine learning. 2025 IEEE High Performance Extreme Computing Conference (HPEC), Wakefield, MA, USA.
Abstract
Edge-cloud systems are heterogeneous computational infrastructures designed to manage distributed workloads efficiently. Accurate prediction of system performance is essential for minimizing execution time, energy, and enhancing throughput. Traditional performance evaluation methods, such as simulation-based tools, are often time-consuming, require manual configuration, and rely heavily on assumptions that limit scalability and adaptability. To address these challenges, this work investigates several machine learning (ML) models to predict performance in heterogeneous edge-cloud environments, focusing on key metrics such as execution time, energy consumption, and throughput. Five different ML models, namely, Random Forest (RF), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and a hybrid RNN-DNN, are developed and evaluated. The training dataset is generated using the VisualSim system-level modeling tool by simulating diverse edge-cloud configurations. The performance of the models is evaluated using the mean absolute error (MAE) and the root mean square error (RMSE), and the predicted results are validated against the VisualSim outputs. Experimental results show that the RF model achieves the lowest MAE and RMSE on the test datasets. The deep learning models exhibit varying levels of accuracy, with the DNN model offering a strong trade-off between computational complexity and predictive performance.
Table of Contents
Description
Click on the DOI link to access this conference paper at the publishers website (may not be free).
Publisher
Institute of Electrical and Electronics Engineers Inc.
Journal
Book Title
Series
2025 IEEE High Performance Extreme Computing Conference (HPEC)
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2643-1971
2377-6943
2377-6943