Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 15172-15186· 0 citations· 42 references
Abstract
Network Function Virtualization (NFV) is a foundational technology for Mobile Edge Computing (MEC). It delivers network services by chaining Virtual Network Functions (VNFs) into sequential Service Function Chains (SFCs). One of the most critical challenges in MEC is how to provide continuous and stable services to high-mobility user, such as intelligent vehicles and drones. However, current mobility-aware SFC migration methods remain constrained by either post-hoc reaction or myopic prediction horizons, failing to reconcile the divergent timescales of network services and user mobility, thus resulting in suboptimal resource allocation and service delivery. In this paper, we first formulate the predictive mobility-aware SFC migration problem as an NP-hard Integer Linear Programming (ILP) problem. Aiming to mitigate service disruption for mobile users in MEC networks, we propose PreSFC, a predictive SFC migration framework that integrates multi-slot mobility forecasting with fine-grained network state tracking. We first design Gformer, a deep learning-based long sequence time-series forecasting model, which operates on short time slots (less than 200 ms) to sensitively capture network dynamics while predicting over multiple slots (e.g., 50 slots) to effectively track user mobility. This dual-scale design explicitly addresses the temporal disparity between mobility patterns and service requirements. Based on the predictions, we further propose an Optimal Sub-period Partitioning Migration (OSPM) algorithm to determine migration timing and locations. Extensive simulations show that our approach reduces the maximum and average downtime by approximately 55% and 40%, respectively, compared to benchmark methods.
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
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks.
H. Asif, Abdulraqeb Alhammadi, N. Tarhuni et al.· Future Internet· 0 citations
Efficient service function chain (SFC) placement is critical for optimizing network service delivery in dynamic cross-domain networks (CDNs), especially under resource-constrained and heterogeneous environments. However, existing approaches face fundamental limitations in achieving effective multi-objective optimization, particularly in balancing latency minimization with efficient resource utilization. These challenges are further compounded by the inability to capture future resource dynamics and limited visibility across multiple domains. To address these challenges, we propose a novel multi-objective framework for SFC placement that jointly considers latency and resource utilization. The framework integrates Transformer-based prediction with linear programming (LP) to explicitly model future deployability, enabling proactive and globally informed placement decisions. In addition, a dynamic modeling mechanism is developed using domain-aware detection and graph autoencoders (GAEs) to capture evolving network topologies and cross-domain structural dependencies. A Pareto-based optimization strategy is further employed to systematically balance latency and resource efficiency across heterogeneous domains and varying workload conditions. Extensive experiments across multiple network scales and diverse SFC configurations demonstrate that the proposed framework achieves a superior trade-off between latency and deployment capability, while improving scalability, robustness, and long-term resource efficiency in dynamic and large-scale CDN environments.
Juan Zhang, Yan-Fei Ma, Xunzheng Zhang et al.· IEEE Transactions on Network...· 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
Developments in 5G New Radio (NR) aim to provide the best possible user experience, but barred and resource-depleted cells in dense small cell networks can cause service interruptions and increase handover latency. To address these challenges, we propose a proactive mitigation framework that applies the unified Autoregressive Recurrent Neural Network (AR-RNN). This framework simultaneously predicts a user’s future cell trajectory and their resource requirements by learning from historical data. By integrating these predictive forecasts with standard network-monitoring alerts (e.g., from an Intrusion Detection System (IDS), a Network Management System (NMS), or Operations, Administration, and Maintenance (OAM) systems), the system preemptively reroutes users to avoid flagged or unavailable cells. Applying the algorithm to both Homogeneous and Heterogeneous networks, our simulations demonstrate that this proactive approach yields substantial and quantifiable improvements in network performance across both topologies. The AR-RNN model achieves a next-cell prediction accuracy of up to 95.8%, establishing high dependability. This accuracy directly translates to enhanced Quality of Service (QoS) by reducing handover latency even to as low as nearly 5 ms. Furthermore, the framework significantly improves network reliability, reducing the network outage probability by up to 50% compared to standard reactive handover procedures. These results demonstrate a concrete and effective method for creating a more seamless and efficient telecommunications experience in dense 5G environments.
Khoa Nguyen Dang Dinh, P. Fazio, Miroslav Voznák· PLoS ONE· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
