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.
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.
Liang Liu, Yejun He, Yujie Zhang et al.· IEEE Transactions on Network...· 0 citations
Network slicing in 5G New Radio (NR) requires the simultaneous satisfaction of heterogeneous Service Level Agreements (SLAs) for ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). The objective of this study is to design and evaluate a dynamic, three-layer radio-resource-management framework, built on the O-RAN Near-Real-Time Radio Intelligent Controller (Near-RT RIC) that meets these three conflicting SLAs concurrently on a shared gNB. At the data plane, three algorithms are proposed: a Weighted Proportional-Fair (WPF) scheduler that maps RIC-issued weights to proportional-fair time windows; a slice-aware pre-processor that estimates per-slice Physical Resource Block (PRB) demand; and a starvation-aware Bandwidth Part (BWP) multiplexer. At the RIC, a heuristic BWP Manager observes per-slice key performance indicators every 100 ms and updates the slice weights through an exponential-moving-average-smoothed proportional update law. The framework is implemented in ns-3 v3.40 with the 5G-LENA NR module, co-simulated with a Python RIC through ns3-gym, and compared against a static-weight TDMA-PF baseline. Preliminary results from a 10-second co-simulation, in which a single gNB serves 5 uRLLC, 10 eMBB and 30 mMTC user equipments across three independent BWPs, show that the dynamic framework attains 100% uRLLC deadline compliance (mean delay 0.49 ms), 100% eMBB throughput success (34.9 Mbps per UE) and zero mMTC packet loss, whereas the static baseline fails all three SLAs. The idealized modelling assumptions and their implications for real deployments are discussed as limitations.
Bini Chand, Aashraya Neupane, Binu Suwal et al.· Journal of Institute of Scie...· 0 citations
This paper proposes a multi-objective Integer Linear Programming (ILP) formulation for optimal virtual Content Delivery Network (vCDN) placement in fixed broadband networks. The proposed framework jointly minimizes backhaul traffic and end-to-end latency across a six-tier topology spanning OLT, Tier 2/Tier 1 aggregation, Provider Edge, Transport Backbone, and International Gateway nodes. Key contributions include a partial caching model with ratio α***(0,1] that increases cache diversity by 1/α (Theorem 1), a view-time threshold mechanism with effective utilization factor ηⱼ, and a placement efficiency metric Φ that quantifies performance gain per unit deployment cost. Proof-of-concept simulation confirms 81.2% average RTT reduction (5.704 ms → 1.075 ms), directly validating the latency component of the ILP objective.
Yohana Jayanti Aruan, R. Munadi, S. Hertiana et al.· International Conference on...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
Real-time inter-slice resource allocation in the Radio Access Network (RAN) is a critical control function in 5G and emerging 6G networks, where the scheduler in the Distributed Unit (DU) dynamically allocates physical resources, namely Physical Resource Blocks (PRBs), to different network slices to meet their diverse Quality of Service (QoS) requirements. To address the need for faster and more flexible radio resource management, and inspired by recent efforts to extend the O-RAN architecture with a real-time controller, we investigate slice-level PRB allocation through the lens of online learning. We formulate inter-slice scheduling as a dynamic decision problem and develop a system model that captures per-slice Service Level Agreement (SLA) requirements and throughput variations over configurable time windows, without assuming future channel knowledge. Our scheduling solution is implemented as a real-time RAN control application, in line with the O-RAN proposition for dApps that are programmable and distributed software components for fine-grained control in O-RAN DUs (O-DUs) and Centralized Units (O-CUs). The proposed approach adapts inter-slice radio resource allocations based on telemetry, with low computational complexity. Experimental results show sublinear dynamic regret, up to 85% fewer SLA violations than static baselines, and submillisecond amortized control overhead. Overall, these findings highlight dynamic-benchmark online control as a practical mechanism for real-time, SLA-aware slicing in O-RAN.
Asim Zoulkarni, C. Papagianni, Georgios Iosifidis et al.· International Mediterranean...· 0 citations
Modern networked systems are progressively adopting network virtualization and Service Function Chaining (SFC) to deliver scalable, flexible and low-latency services. A central challenge in this context is the Service Function Chain Placement Problem (SFC-PP), which involves efficiently placing Virtualized Network Functions (VNFs) under stringent resource constraints and Quality of Service (QoS) requirements in dynamic network environments. To address this, we propose an enhanced Proximal Policy Optimization (PPO) framework for resource-aware and latency-sensitive SFC placement in edge-enabled networks. Our approach incorporates network performance metrics into the PPO learning pipeline through delay-aware reward design, resource-aware state representation and feasibility-aware action masking, thereby improving policy stability and infrastructure utilization. Extensive evaluations on multiple topologies including RNET, NSFNET, GBN and GEANT2 demonstrate that the proposed PPO significantly outperforms the Random Selection (RS), Q-Learning (QL) and Deep Q-Network (DQN) baselines, achieving up to 31% higher service acceptance, with reduced service latency and improved resource efficiency. A healthcare-oriented case study demonstrates the applicability of the proposed framework in mission-critical and latency-sensitive service environments. These results highlight PPO as a robust and scalable solution for intelligent service orchestration in dynamic network environments.
Nithin Melala Eshwarappa, Ching-Hsien Hsu, Hojjat Baghban et al.· ACM Transactions on Modeling...· 0 citations