Abstract
Beyond 5G and 6G networks are foreseen to be highly dynamic. These are expected to support and accommodate temporary activities and leverage continuously changing infrastructures from extreme edge to cloud. In addition, the increasing demand for applications and data in these networks necessitates the use of geographically distributed Multi-access Edge Computing (MEC) to provide reliable services with low latency and energy consumption. Service management plays a crucial role in meeting this need. Research indicates widespread acceptance of Reinforcement Learning (RL) in this field due to its ability to model unforeseen scenarios. However, it is difficult for RL to handle exhaustive changes in the requirements, constraints and optimization objectives likely to occur in widely distributed networks. Therefore, the main objective of this research is to design service management approaches to handle changing services and infrastructures in dynamic distributed MEC systems, utilizing advanced RL methods such as Distributed Deep Reinforcement Learning (DDRL) and Meta Reinforcement Learning (MRL).
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Acknowledgement
This work has been performed in the framework of the EU’s H2020 project AI@EDGE (101015922). The authors would also like to acknowledge the CERCA Programme/Generalitat de Catalunya, the EU “NextGenerationEU/PRTR”, MCIN and AEI (Spain) under project IJC2020-043058-I, and by MCIN/AEI/10.13039/501100011033 (FEDER, EU) under grant PID2022-142332OA-I00.
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Torres-Pérez, C., Coronado, E., Cervelló-Pastor, C., Siddiqui, M.S. (2024). Service Management in Dynamic Edge Environments. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14352. Springer, Cham. https://doi.org/10.1007/978-3-031-48803-0_38
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DOI: https://doi.org/10.1007/978-3-031-48803-0_38
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