End-to-End Routing for Jointly Ultra-Service and Regular-Service Flows in TSN: An Evolutionary Transformer-Based DRL Approach

End-to-end delay performance analysis of the 5G-TSN network using network calculus

An improved strict-priority Deficit Round-Robin (SP-DRR) scheduling strategy is proposed and incorporates it into a unified moment generating function (MGF) analytical framework, referred to as SP-DRR-MGF, for probabilistic E2E delay analysis in 5G–TSN networks.

Cross-Layer Scheduling Optimization of 5G-TSN Integrated Networks Based on QoS Mapping and Joint Scheduling

Simulation results demonstrate that the proposed QoS-mapping-based no-wait latency-balanced joint scheduling (QMLB-JS) algorithm improves the end-to-end deterministic transmission capability of the integrated 5G-TSN network.

B-CUBE: a burst-aware 5G-TSN architecture and its large-scale mixed-flow scheduling

AVB-Aware Optimized Routing and Scheduling of Time-Triggered Traffic in Time-Sensitive Networks

Time-Sensitive Networking (TSN) supports mixed-criticality communication by integrating Time-Triggered (TT) and Audio Video Bridging (AVB) traffic within a unified network infrastructure. While TT flows benefit from deterministic scheduling through the Time-Aware Shaper (TAS), their presence can increase the worst-case delay (WCD) experienced by AVB traffic. However, many existing AVB-aware TT scheduling approaches incur high computational costs and lack a theoretical foundation for analyzing the impact of TT routing on AVB performance. To address these limitations, this article presents a unified routing and scheduling framework that jointly optimizes TT communication while systematically improving AVB performance. At the core of our method is a network calculus-based analysis that derives a theoretical lower bound on AVB WCD under TT interference. This bound is consistently leveraged in both the routing and scheduling stages: first, to define a flow-level metric called Impact on WCD (IoW) that guides AVB-aware routing decisions; and second, to introduce an AVB-Aware Idle Constraint that regulates TT offsets to shape residual bandwidth for AVB traffic. Extensive experiments across diverse topologies and traffic patterns demonstrate that the proposed framework significantly improves AVB schedulability and delay bounds while maintaining TT feasibility with low computational overhead. These results confirm the practicality and effectiveness of a tightly integrated approach to TSN configuration for mixed-criticality systems.

Multi-Source Multicast SFCs Embedding in Space-Air-Ground Integrated Networks

Space-air-ground integrated networks (SAGINs) offer seamless three-dimensional coverage and strengthened flexibility, which are recognized as a core network architecture of 6G. Software-defined networking (SDN) and network function virtualization (NFV) are two enabling technologies for SAGINs that can be utilized to sequentially arrange virtual network functions (VNFs) into service function chains (SFCs) to provide users with resource-efficient and delay-optimized multi-source multicast request (MMR) services. However, SAGINs exhibit significant dynamism and heterogeneity, it brings great challenges when dynamically deploying the MMR’s source nodes and SFCs for fulfilling MMR routing. This paper investigates the multi-source multicast SFC embedding problem (MMSEP) considering the determination of the source nodes for MMR, VNFs placement, as well as network resources and delay constraints in the SDN/NFV-enabled SAGIN. Firstly, we define and formulate the MMSEP and demonstrate its NP-hardness. Subsequently, we employ a heuristic algorithm to assign the optimal source nodes for all multicast destination nodes and utilize the markov decision process (MDP) to simulate dynamic transitions in network states. Finally, we propose a deep deterministic policy gradient with attention mechanism (DDPG-AM) to address the MMSEP, aiming to minimize resource consumption costs and delays while maximizing the revenue of the internet service provider. The simulation results demonstrate that the proposed algorithm surpasses the state-of-the-art DDPG algorithm by approximately 27% in network utility, 17% in latency reduction, and 5% in acceptance ratio.

Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs

The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators'profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.