Intelligent Resource Orchestration for 5G Edge Infrastructures

Rafael Moreno-Vozmediano (), Rubén S. Montero, Eduardo Huedo and Ignacio M. Llorente
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Rafael Moreno-Vozmediano: Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
Rubén S. Montero: Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
Eduardo Huedo: Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
Ignacio M. Llorente: OpenNebula Systems, Paseo del Club Deportivo 1, Pozuelo de Alarcón, 28223 Madrid, Spain

Future Internet, 2024, vol. 16, issue 3, 1-31

Abstract: The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

Keywords: 5G edge infrastructures; intelligent edge orchestration; workload forecasting; resource allocation and optimization; machine learning; integer linear programming (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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