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- Title
Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum.
- Authors
Poltronieri, Filippo; Stefanelli, Cesare; Tortonesi, Mauro; Zaccarini, Mattia
- 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.
- Subjects
COMPUTATIONAL intelligence; REINFORCEMENT learning; OPTIMIZATION algorithms; INTELLIGENCE service; PARTICLE swarm optimization; QUANTUM computers; TEXTILE technology
- Publication
Future Internet, 2023, Vol 15, Issue 11, p359
- ISSN
1999-5903
- Publication type
Article
- DOI
10.3390/fi15110359