An online ensemble method for auto-scaling NFV-based applications in the edge

Autor(es)

da Silva, Thiago Pereira ; Batista, Thais Vasconcelos ; Delicato, Flavia Coimbra ; Pires, Paulo Ferreira

• Tipo de documento: Artigo Científico
• Fonte: Cluster Computing-The Journal of Networks Software Tools and Applications; v. 27, n. 4, p. 25-pg., 2024-04-30.

Resumo

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms struggle with streaming data as they rely on static datasets for model construction. In contrast, Online Machine Learning (OML) adapts to changing environments by training the model with each new observation in real-time. However, developing OML algorithms introduces complexities such as bias and variance considerations, making the selection of suitable estimators challenging. In this challenging landscape, ensemble learning emerges as a promising approach, offering a strategic framework to navigate the bias-variance tradeoff and enhance prediction accuracy by amalgamating outputs from diverse ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge, thereby empowering IoT applications with robust decision-making capabilities. Our study addresses the critical challenges of ML in resource-constrained edge computing environments, offering practical insights for enhancing predictive accuracy and scalability in IoT applications. To validate our ensemble's efficacy, we conducted comprehensive experimental evaluations leveraging both synthetic and real-world datasets. The results indicate that our ensemble surpassed state-of-the-art data stream algorithms and ensemble regressors across a range of regression metrics, underlining its superior predictive prowess. Furthermore, we scrutinized the ensemble's performance within the realm of auto-scaling for Virtual Network Function (VNF)-based applications situated at the network's edge, thereby elucidating its applicability and scalability in real-world scenarios. (AU)

• Processo FAPESP: 15/24144-7 - Tecnologias e soluções para habilitar o paradigma de nuvens de coisas
• Beneficiário: José Neuman de Souza
• Modalidade de apoio: Auxílio à Pesquisa - Temático