Future sixth-generation (6G) networks will rely on the synergies of edge computing and machine learning (ML) to build an intelligent edge, where communication and computing resources will be jointly orchestrated. In this work, we leverage ML algorithms to judiciously orchestrate the placement of delay-constrained computing tasks in a softwarized edge domain. A set of popular supervised learning algorithms, i.e., Decision Tree (DT), Bagged Trees (BTs), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), have been leveraged to this purpose. They are trained off-line through the results of an optimization problem targeting the minimization of the edge network resource usage while respecting the tasks’ delay constraints. Extensive simulation results are reported to showcase the performance of the considered techniques in terms of model accuracy, complexity and network-related metrics, e.g., amount of exchanged data in the edge domain. Among the compared techniques, DT and MLP are shown to be the most efficient solutions in terms of algorithm execution time, by achieving almost the same performance.

In-network placement of delay-constrained computing tasks in a softwarized intelligent edge

Lia G.;Amadeo M.;Ruggeri G.;Campolo C.;
2022-01-01

Abstract

Future sixth-generation (6G) networks will rely on the synergies of edge computing and machine learning (ML) to build an intelligent edge, where communication and computing resources will be jointly orchestrated. In this work, we leverage ML algorithms to judiciously orchestrate the placement of delay-constrained computing tasks in a softwarized edge domain. A set of popular supervised learning algorithms, i.e., Decision Tree (DT), Bagged Trees (BTs), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), have been leveraged to this purpose. They are trained off-line through the results of an optimization problem targeting the minimization of the edge network resource usage while respecting the tasks’ delay constraints. Extensive simulation results are reported to showcase the performance of the considered techniques in terms of model accuracy, complexity and network-related metrics, e.g., amount of exchanged data in the edge domain. Among the compared techniques, DT and MLP are shown to be the most efficient solutions in terms of algorithm execution time, by achieving almost the same performance.
6G
Edge AI
Edge computing
In-network computing
Intelligent edge
Machine learning
Task placement
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/131467
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