Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

Filippo Poltronieri, Cesare Stefanelli, Mauro Tortonesi () and Mattia Zaccarini
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Filippo Poltronieri: Department of Engineering, University of Ferrara, 44122 Ferrara, Italy
Cesare Stefanelli: Department of Engineering, University of Ferrara, 44122 Ferrara, Italy
Mauro Tortonesi: Department of Mathematics and Computer Science, University of Ferrara, 44121 Ferrara, Italy
Mattia Zaccarini: Department of Engineering, University of Ferrara, 44122 Ferrara, Italy

Future Internet, 2023, vol. 15, issue 11, 1-30

Abstract: Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In this paper, we make a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario. Specifically, this comparison included the Deep Q-Network, Proximal Policy Optimization, Genetic Algorithms, Particle Swarm Optimization, Quantum-inspired Particle Swarm Optimization, Multi-Swarm Particle Optimization, and the Grey-Wolf Optimizer. We demonstrate how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization. Finally, we show that, if the scenario conditions change, Deep-Reinforcement-Learning-based approaches can exploit the experience built during training to adapt service allocation according to the modified conditions.

Keywords: Cloud Continuum; IT service management; service fabric management; resource management; Computational Intelligence; Reinforcement Learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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