Cascaded Transformer for Robust and Scalable SLA Decomposition via Amortized Optimization

• URL: http://arxiv.org/abs/2601.11859v1
• Date: Sat, 17 Jan 2026 01:01:53 GMT
• Title: Cascaded Transformer for Robust and Scalable SLA Decomposition via Amortized Optimization
• Authors: Cyril Shih-Huan Hsu,
• Abstract summary: 6G networks increasingly rely on network slicing to provide tailored, End-to-End (E2E) logical networks over shared physical infrastructures.<n>Current solutions handle through computationally intensive, iterative optimization processes that incur substantial latency and complexity.<n>We introduce Casformer, a cascaded Transformer architecture designed for fast, optimization-free SLA decomposition.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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