Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning

In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and extensive 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 orchestrating complex network services. Nevertheless, partitioning SFCs across multi-domain network infrastructures presents substantial challenges due to stringent latency constraints and limited resource availability. Conventional optimization-based methods typically exhibit low scalability, whereas existing data-driven approaches often fail to adequately balance computational efficiency with the capability to effectively account for dependencies inherent 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 among VNFs, facilitating coordinated and parallelized decision-making processes. Additionally, we enhance training stability and convergence using $\epsilon$-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 acceptance rates, resource utilization efficiency, and scalability, while achieving rapid inference. This study not only advances intelligent network orchestration by delivering a scalable and robust solution for SFC partitioning within emerging 6G environments, but also bridging recent advancements in Large Language Models (LLMs) with the optimization of next-generation networks.

NetROS-5G: Enhancing Personalization through 5G Network Slicing and Edge Computing in Human-Robot Interactions

Robots are increasingly being used in a variety of applications, from manufacturing and healthcare to education and customer service. However, the mobility, power, and price points of these robots often dictate that they do not have sufficient computing power on board to run modern algorithms for personalization in human-robot interaction at desired rates. This can limit the effectiveness of the interaction and limit the potential applications for these robots. 5G connectivity provides a solution to this problem by offering high data rates, bandwidth, and low latency that can facilitate robotics services. Additionally, the widespread availability of cloud computing has made it easy to access almost unlimited computing power at a low cost. Edge computing, which involves placing compute resources closer to the action, can offer even lower latency than cloud computing. In this paper, we explore the potential of combining 5G, edge, and cloud computing to provide improved personalization in human-robot interaction. We design, develop, and demonstrate a new framework, entitled NetROS-5G, to show how the performance gained by utilizing these technologies can overcome network latency and significantly enhance personalization in robotics. Our results show that the integration of 5G network slicing, edge computing, and cloud computing can collectively offer a cost-efficient and superior level of personalization in a modern human-robot interaction scenario.