Network automation is gaining significant attention in the development of B5G networks, primarily for reducing operational complexity, expenditures and improving network efficiency. Concurrently operating closed loops aiming for individual optimization targets may cause conflicts which, left unresolved, would lead to significant degradation in network Key Performance Indicators (KPIs), thereby resulting in sub-optimal network performance. Centralized coordination, albeit optimal, is impractical in large scale networks and for time-critical applications. Decentralized approaches are therefore envisaged in the evolution to B5G and subsequently, 6G networks. This work explores pervasive intelligence for conflict resolution in network automation, as an alternative to centralized orchestration. A Q-Learning decentralized approach to network automation is proposed, and an application to network slice auto-scaling is designed and evaluated. Preliminary results highlight the potential of the proposed scheme and justify further research work in this direction.

中文翻译：

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用于 B5G 网络自动化中冲突解决的 Q-Learning

网络自动化在 B5G 网络的开发中受到了极大的关注，主要是为了降低运营复杂性、支出和提高网络效率。同时运行针对各个优化目标的闭环可能会导致冲突，如果未解决，将导致网络关键性能指标 (KPI) 显着下降，从而导致网络性能欠佳。集中协调虽然是最佳的，但在大规模网络和时间关键型应用中是不切实际的。因此，在向 B5G 和随后的 6G 网络演进中设想了去中心化方法。这项工作探索了网络自动化中解决冲突的普遍智能，作为集中编排的替代方案。提出了一种用于网络自动化的 Q-Learning 分散式方法，并设计和评估了网络切片自动缩放的应用程序。初步结果突出了所提议方案的潜力，并证明在这个方向上进一步研究工作是合理的。