The evolution toward 6G networks increasingly relies on network slicing to provide tailored, End-to-End (E2E) logical networks over shared physical infrastructures. A critical challenge is effectively decomposing E2E Service Level Agreements (SLAs) into domain-specific SLAs, which current solutions handle through computationally intensive, iterative optimization processes that incur substantial latency and complexity. To address this, we introduce Casformer, a cascaded Transformer architecture designed for fast, optimization-free SLA decomposition. Casformer leverages historical domain feedback encoded through domain-specific Transformer encoders in its first layer, and integrates cross-domain dependencies using a Transformer-based aggregator in its second layer. The model is trained under a learning paradigm inspired by Domain-Informed Neural Networks (DINNs), incorporating risk-informed modeling and amortized optimization to learn a stable, forward-only SLA decomposition policy. Extensive evaluations demonstrate that Casformer achieves improved SLA decomposition quality against state-of-the-art optimization-based frameworks, while exhibiting enhanced scalability and robustness under volatile and noisy network conditions. In addition, its forward-only design reduces runtime complexity and simplifies deployment and maintenance. These insights reveal the potential of combining amortized optimization with Transformer-based sequence modeling to advance network automation, providing a scalable and efficient solution suitable for real-time SLA management in advanced 5G-and-beyond network environments.

翻译：面向6G网络的演进日益依赖网络切片技术，在共享物理基础设施上提供定制化的端到端（E2E）逻辑网络。一个关键挑战在于如何将E2E服务等级协议（SLA）有效分解为域特定SLA，现有解决方案通常通过计算密集的迭代优化过程实现，这会带来显著延迟和复杂度。为此，我们提出Casformer——一种专为快速、免优化的SLA分解设计的级联Transformer架构。Casformer在第一层通过域特定Transformer编码器利用历史域反馈信息，在第二层通过基于Transformer的聚合器整合跨域依赖关系。该模型在受域感知神经网络（DINNs）启发的学习范式下进行训练，融合风险感知建模与摊销优化技术，以学习稳定、仅前向的SLA分解策略。大量实验表明，相较于基于优化的前沿框架，Casformer在SLA分解质量上表现更优，同时在动态嘈杂的网络条件下展现出更强的可扩展性与鲁棒性。此外，其纯前向设计降低了运行时复杂度，简化了部署与维护流程。这些发现揭示了将摊销优化与基于Transformer的序列建模相结合推动网络自动化的潜力，为先进5G及未来网络环境中的实时SLA管理提供了可扩展且高效的解决方案。