In the upcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective management of Virtualized Network Functions (VNFs) is essential. VNFs are software-based counterparts of traditional hardware devices that facilitate flexible and scalable service provisioning. Service Function Chains (SFCs), structured as ordered sequences of VNFs, are pivotal in delivering complex network services.
However, splitting an SFC into multiple segments deployed across different network domains or infrastructure locations presents substantial challenges due to potential heterogeneity of domain characteristics, quality of service (QoS) constraints, and limited visibility of network state. Conventional optimization methods have limited scalability, while existing data-driven approaches struggle to balance efficiency with capturing VNF inter-dependencies in SFCs.
To overcome these limitations, we introduce a Transformer-empowered actor-critic framework specifically designed for sequence-aware SFC partitioning. By utilizing the self-attention mechanism, our approach effectively models complex inter-dependencies between VNFs, facilitating coordinated and parallel decision-making processes. Furthermore, to improve training stability and convergence, we introduce an $\epsilon$-LoPe exploration strategy as well as Asymptotic Return Normalization.
Comprehensive simulation results demonstrate that the proposed methodology outperforms existing state-of-the-art solutions in terms of long-term service acceptance rates, resource utilization, and scalability while achieving fast inference.
Blogger's Review: This paper presents a robust Transformer-based framework for tackling the complex service function chain partitioning problem, especially in dynamic network environments. By introducing innovative exploration strategies and normalization techniques, it significantly enhances training stability and efficiency, laying the groundwork for future 6G network service management.