TL;DR
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.
Context. The rapid deployment of 5G networks and the emergence of 6G architectures introduce unprecedented traffic heterogeneity and burstiness across radio, edge, and core domains. Meanwhile, the energy footprint of mobile infrastructure is becoming a major sustainability concern, as carbon emissions increasingly shape network operation policies.Objective. This work aims to design a predictive carbon-aware multi-layer resource slicing framework for RAN – edge – core 5G/6G networks that jointly optimizes latency, cost, energy, and carbon emissions under bursty traffic conditions.Method. The proposed approach integrates an M/G/1-based queuing model for accurate representation of heavy-tailed service times and bursty arrival patterns; hybrid short-term/long-term forecasting of both traffic load and regional carbon intensity; and multi-objective optimization for carbon-aware VNF placement and traffic steering across network layers. A proactive – reactive orchestration mechanism performs predictive resource pre-allocation and runtime scaling.Results. Trace-driven simulations on a representative multi-layer testbed demonstrate a 34% reduction in CO2 emissions compared to latency-first orchestration, alongside a 22% decrease in operational cost and <1% SLA violation rate. Tail latency remains within slice-specific thresholds even under bursty loads, confirming that carbon reductions can be achieved without service degradation.Conclusions. Predictive, carbon-aware orchestration across RAN-edge-core domains substantially improves environmental and economic efficiency while preserving QoS guarantees. The results highlight the importance of integrating forecast-drivenoptimization and realistic traffic modeling into next-generation slicing architectures.
Fifth generation (5G) networks deliver multi-gigabit data rates, sub-millisecond latency, and dense connectivity through a customised service delivery paradigm built on virtualisation and network slicing (NS). However, conventional NS frameworks rely on threshold-based control and lack the context awareness needed for autonomous, user-centric decision-making. Machine learning (ML) optimization-driven methods such as deep reinforcement learning (DRL) and hybrid metaheuristic–ML approaches can close this gap by inferring user bandwidth behaviour, anticipating congestion, and enacting proactive corrective actions. This paper presents a systematic, PRISMA-based review of 2024–2026 ML-based optimization for user-centric distributed NS, screening 6,286 records to 67 core studies that are normalised through a common evidence tuple. A comprehensive critical review is then presented with role-oriented matrices spanning admission control, resource allocation and offloading, orchestration, graph learning, and federated learning (FL). From this synthesis, we derive recurring optimization formulations and identify persistent gaps, namely the absence of direct Quality of Experience (QoE) inference, privacy preservation, topology awareness, and validated deployment. To address these gaps, we propose a QoE-aware framework for Multi-Access Edge Computing (MEC)-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. The proposed framework is also grounded in preliminary validation from our two prior slice admission control and load balancing studies, offering a scalable, privacy-aware, and truly user-centric path towards 5G and Beyond 5G networks.
The growth of Internet of Things (IoT) applications and latency-sensitive services has increased the demand for efficient service placement across compute continuum platforms, such as edge--cloud systems. Modern applications are decomposed into interdependent microservices deployed over heterogeneous infrastructures, making placement under resource and network constraints an intractable NP-hard combinatorial optimisation problem. This study proposes a latency-aware Edge Placement Neural Combinatorial Optimiser (EP-NCO), a learning-based framework for service placement in compute continuum platforms. EP-NCO employs a dual-graph model to capture resource relationships and service dependencies within both computing infrastructure and application structure. Graph neural networks (GNNs) learn structural embeddings of infrastructure nodes and service components, whereas reinforcement learning policies construct feasible placements that account for execution latency, communication link delays, and bandwidth-sharing effects. Extensive simulations across multiple system scales demonstrate that EP-NCO consistently achieves high-quality placement decisions, reducing the total service response time by 46%--50% compared with metaheuristics (genetic algorithm and particle swarm optimisation) and by 25%--35% compared with controlled RL ablation baselines. Once trained, EP-NCO enables fast online inference, making it a practical solution for dynamic large-scale edge--cloud environments with hundreds of computing nodes, hosting thousands of applications, which is significantly beyond the capability of current scheduling systems.
The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.
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.
The advent of sixth-generation (6G) wireless communication networks introduces unprecedented demands for ultra-high data rates, near-zero latency, massive device connectivity, and hyper-reliability. To satisfy these heterogeneous service requirements simultaneously, network slicing has emerged as a foundational architectural paradigm. However, the static and reactive resource allocation strategies utilized in legacy generations are insufficient to cope with the highly dynamic, multi-dimensional, and time-varying nature of 6G traffic demands. This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems. We investigate the application of advanced machine learning paradigms specifically deep reinforcement learning (DRL), federated learning (FL), and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement. Through a robustly designed methodology combining centralized global coordination with decentralized edge execution, this study provides an optimized, self-healing, and intent-driven network orchestration architecture. The experimental evaluation illustrates that the proposed AI-driven framework outperforms conventional heuristics and static optimization models across critical performance metrics, including spectrum efficiency, SLA satisfaction rate, and computational overhead. The paper concludes by defining prominent research gaps and future directions toward standardizing fully autonomous, zero-touch 6G network management.