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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Filippo Poltronieri & Cesare Stefanelli & Mauro Tortonesi & Mattia Zaccarini, 2023. "Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum," Future Internet, MDPI, vol. 15(11), pages 1-30, October.
  • Handle: RePEc:gam:jftint:v:15:y:2023:i:11:p:359-:d:1271905
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    References listed on IDEAS

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    2. Cheng Qian & Xing Liu & Colin Ripley & Mian Qian & Fan Liang & Wei Yu, 2022. "Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions," Future Internet, MDPI, vol. 14(2), pages 1-25, February.
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