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An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks

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Abstract

Future applications to be supported by 6G networks are envisaged to be realized by loosely-coupled and independent microservices. In order to achieve an optimal deployment of applications, smart resource management strategies will be required, working in a cost-effective and resource-efficient manner. Current cloud computing services are challenged to meet the explosive growth and demand of future use cases such as virtual/augmented/mixed reality (VR/AR/MR). The purpose of edge computing (EC) is to better address latency and transmission requirements of those future stringent applications. However, a high flexibility and a rapid decision-making will be required since EC suffers from limited resources availability. For this reason, this work proposes an artificial intelligence (AI) technique, based on reinforcement learning (RL), to make intelligent decisions on the optimal tier and edge-site selection to serve any request according to the application’s category, constraints, and conflicting costs. In addition, when deployed at the edge-network, a heuristic has been proposed for the mapping of microservices within the selected edge-site. That heuristic will exploit a ranking methodology based on the network topology and available network and compute resources while preserving the revenue of the mobile network operator (MNO). Simulation results show that the performance of the proposed RL approach is close to the optimal solution by reaching the cost minimization objective within a 8.3% margin; moreover, RL outperforms considered benchmark algorithms in most of the conducted experiments.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work has been partially funded by the project "UNICO-5 G I+D-OPTIMAIX - TSI -063000-2021-34." This work is also supported by the research group "Management, Pricing and Services in Next Generation Networks" (MAPS) of the Network Engineering Department of the Universitat Politècnica de Catalunya.

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  1. Department of Network Engineering, Universitat Politecnica de Catalunya, Carrer de Jordi Girona,33, 808034, Barcelona, Spain

    John Bosco Ssemakula, Juan-Luis Gorricho, Godfrey Kibalya & Joan Serrat-Fernandez

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  1. John Bosco Ssemakula
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  2. Juan-Luis Gorricho
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  3. Godfrey Kibalya
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Correspondence to John Bosco Ssemakula.

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Ssemakula, J.B., Gorricho, JL., Kibalya, G. et al. An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09643-9

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