[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81528-en":3,"doc-seo-81528-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81528,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782109480056885918",8,"Research & Report","Transformer Empowered Actor Critic Reinforcement Learning for Sequence Aware Service Function Chain Partitioning","In the era of 6G, effective management of Virtualized Network Functions (VNFs) is essential for ultra-low latency and ubiquitous connectivity. Service Function Chains (SFCs), modeled as ordered sequences of VNFs, must be partitioned and deployed across heterogeneous network domains under QoS/SLA constraints and limited network-state visibility. Existing optimization approaches scale poorly and data-driven methods fail to capture VNF inter-dependencies. A transformer-empowered actor-critic framework is proposed to model sequence-aware dependencies and enable coordinated decisions, enhanced with ε-LoPe exploration and Asymptotic Return Normalization. Simulations show improved long-term acceptance, resource utilization, and scalability with fast inference.","Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning  \nCyril Shih-Huan Hsu, Anestis Dalgkitsis, Paola Grosso, Chrysa Papagianni  \narXiv :2504 . 18902v 3 [ cs .NI] 9 Jul 2026  \nAbstract—In the forthcoming 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 softwarebased 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. Nevertheless, splitting an SFC into multiple segments that are deployed across different network domains or infrastructure locations presents substantial challenges due to the potential heterogeneity of domain characteristic along with quality of service (QoS) constraints and limited visibility of network state. Conventional optimization methods have limited scalability, while existing datadriven 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 ϵ-LoPe exploration strategy as well as Asymptotic Return Normalization. Comprehensive simulation results demonstrate that the proposed methodology outperforms existing state-ofthe-art solutions in terms of long-term service acceptance rates, resource utilization, and scalability while achieving fast inference.  \nIndex Terms—service function chain partitioning, service optimization, network function virtualization, quality of service, transformers, deep reinforcement learning  \nI. INTRODUCTION  \nTHE rapid emergence of beyond-5G and 6G networks has  \nmade network slicing and logical topology and resource abstraction central challenges in the optimization of network resources and services. Network services compete for a limited pool of resources over the shared infrastructure to ensure robust Quality of Service (QoS) and meet stringent Service Level Agreements (SLA) . New services not only demand higher cross-domain performance guarantees but also operate in highly shared, multi-tenant environments, imposing significant pressure on both pre-deployment and post-deployment optimization processes. In this context, service partitioning (i.e., distributing service functions across domains) becomes critical. By accounting for constraints, such as limited visibility of the network state, due to privacy regulations or domain  \nUniversity of Amsterdam, LAB42, Science Park 900, 1098 XH Amsterdam, The Netherlands (e-mail: [s.h.hsu@uva.nl](s.h.hsu@uva.nl), [a.dalgkitsis@uva.nl](a.dalgkitsis@uva.nl), [c.papagianni@uva.nl](c.papagianni@uva.nl), [p.grosso@uva.nl](p.grosso@uva.nl)).  \nspecific data governance policies, as well as controlled costdriven overbooking, network operators can preserve performance and meet SLA commitments.  \nThe 3rd Generation Partnership Project (3GPP) introduced End-to-End (E2E) logical network segmentation to enable perservice customization and management, powered by a range of technologies [1] . At its core, Software-Defined Networking (SDN) allows logically centralized, programmatic control of data flows. ETSI has been standardizing Network Functions Virtualization (NFV) that decouples Network Functions (NF) from dedicated hardware and enables scaling services as network demands evolve [2] . Service Function Chaining (SFC) adds another layer of dynamism, allowing for the dynamic linking of multiple NFs to create flexible, policy-driven services. An","cbCaibG22fvpMZsp","https://ap.wps.com/l/cbCaibG22fvpMZsp","pdf",7702080,1,19,"English","en",105,"# Introduction\n## Motivation and background\n## SFC and partitioning problem framing\n## Related enabling technologies and challenges","[{\"question\":\"What problem does the document address in 6G networks?\",\"answer\":\"It addresses how to partition a Service Function Chain into segments deployed across multiple domains while meeting QoS/SLA constraints under limited visibility and domain heterogeneity.\"},{\"question\":\"How does the proposed method incorporate sequence information?\",\"answer\":\"It uses a transformer self-attention mechanism to model complex inter-dependencies between VNFs, enabling sequence-aware, coordinated decision-making.\"},{\"question\":\"Which techniques are introduced to improve training and convergence?\",\"answer\":\"It introduces an ε-LoPe exploration strategy and Asymptotic Return Normalization to enhance training stability and 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problem does the document address in 6G networks?","Question",{"text":74,"@type":75},"It addresses how to partition a Service Function Chain into segments deployed across multiple domains while meeting QoS/SLA constraints under limited visibility and domain heterogeneity.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method incorporate sequence information?",{"text":79,"@type":75},"It uses a transformer self-attention mechanism to model complex inter-dependencies between VNFs, enabling sequence-aware, coordinated decision-making.",{"name":81,"@type":72,"acceptedAnswer":82},"Which techniques are introduced to improve training and convergence?",{"text":83,"@type":75},"It introduces an ε-LoPe exploration strategy and Asymptotic Return Normalization to enhance training stability and 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