Jun 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 32 references
Medicine
Abstract
Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment.
This work investigates the computational footprint of containerized 5G components deployed on a virtualized infrastructure, and leverages this profiling to propose an intelligent orchestration mechanism to maximize both the capacity offered to mobile network users and resource availability to application level services deployed on the computing infrastructure. We deployed and configured containerized 5G core network and radio access components using Open5GCore and UERANSIM, analyzing their resource occupation on the host server. The results show that the User Plane Function and the virtualized gNodeB introduce the most significant processing overhead. For instance, as the system scales from 5 users with 5 Mbps channels to 100 users with 50 Mbps channels, we observe an increase in CPU utilization of approximately 522%, highlighting the strong dependency between traffic load and resource consumption. Then, we incorporate the measured resource profiles into a state-of-the-art service orchestration system capable of deploying both 5G core network components and application-level services on heterogeneous computing equipment. Using an appropriate simulation tool, we evaluate our intelligent scheduling algorithm for service placement and compare it with relevant heuristic strategies. Results demonstrate that the proposed approach reduces service blocking probability by up to 76.77% while improving the bandwidth delivered to users by up to 46.76%, depending on service arrival rates and the number of users connected to the network. Based on these findings, we conclude that the proposed approach proves helpful in improving both resource efficiency and service quality.
Gaetano Francesco Pittalà, G. Davoli, Walter Cerroni et al.· IEEE Conference on Network S...· 0 citations
As Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enabled networks scale in size and complexity, monitoring and managing Service Function Chains (SFCs) under stringent latency and resource constraints becomes increasingly challenging. Although Deep Reinforcement Learning (DRL) is widely applied to SFC provisioning and Virtual Network Function (VNF) placement, enhanced network state monitoring is crucial to capture unexpected network conditions and guide DRL agents toward more adaptive decisions. In this context, Language Models (LMs) enable flexible, natural-language (NL)–based, query-driven network monitoring; however, directly processing complex multi-metric NL queries is computationally expensive and error-prone. This paper proposes an end-to-end (E2E) edge-based query translation pipeline that decomposes multi-metric NL queries into simpler single-metric sub-queries. Query decomposition is performed using a retrieval-augmented language model (RAG-LLM) and compared with a lightweight rule-based decomposition baseline. The resulting sub-queries are translated into Structured Query Language (SQL) using FLAN-T5. A cloud-only baseline, which directly translates NL queries to SQL without decomposition, is also evaluated. The results show that the rule-based edge pipeline achieves the lowest latency, reducing E2E latency by up to 78% compared to RAG-LLM and 18% compared to cloud execution under high workloads. Under increasing arrival rates for the largest workload, the rule-based edge pipeline maintains superior performance over cloud, reducing total E2E latency by 57% at $\lambda = 0.8$ . While RAG-LLM provides greater flexibility for unseen query patterns, both edge-based approaches achieve 100% NL2SQL accuracy with zero decomposition failures, outperforming the cloud-only baseline (95% accuracy).
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan et al.· IEEE Transactions on Network...· 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
In recent years, the number of Internet-connected devices has increased notably, leading to a significant rise in data traffic. This increase has been fostered by the Internet of Things paradigm, the use of microservices architectures in application development, and the ability to deploy these applications across various layers in the Computing Continuum (including Fog, Edge, and Cloud layers). Consequently, choosing the right deployment strategy has become essential for network operators and developers, especially in intensive domains such as smart cities. In this work, we introduce an emulation framework that allows developers and operators to decide how to deploy networks, computing devices, and applications in a Computing Continuum environment, ensuring compliance with established Quality of Service standards. This framework supports both IP and SDN network paradigms and is highly adaptable to different scenarios due to its use of container-based virtualization. Furthermore, the SDN paradigm provides flexibility, enabling the implementation of a service discovery feature that simplifies communication between end devices and services. Evaluations conducted in a realistic smart city scenario show that this framework can be extended and applied to a wide range of situations and configurations, meeting the needs of the research community in the Computing Continuum domain.
José Gómez-delaHiz, J. Herrera, S. Laso et al.· Infocommunications journal· 0 citations
The emergence of sixth-generation (6 G) networks demands a radical shift in how telecommunication infrastructures are designed, managed, and operated. This paper presents a comprehensive vision for an evolvable 6 G telco cloud and service provisioning platform that supports multi-provider, multi-technology environments across distributed sites. The proposed infrastructure integrates disaggregated network functions, open-source frameworks, AI-native orchestration, and end-to-end virtualization to deliver advanced services such as AI-as-a-Service (AIaaS), ISAC, Compute-as-a-Service (CaS), and Security-as-a-Service (SecaaS). Emphasizing sustainability, scalability, and openness, the platform enables dynamic service delivery, supports vertical-specific use cases, and validates performance against stringent 6G Key Performance Indicators (KPIs) and societal Key Value Indicators (KVIs). To validate the proposed platform, we present a simulation study of Service Function Chaining (SFC) latency path optimization across four representative 6 G service chains: Ultra-Reliable Low-Latency Communications (URLLC), Integrated Sensing and Communication (ISAC)+AIaaS, SecaaS+CaaS, and Holographic, spanning far-edge, near-edge, and core-cloud layers. Results show that, under the baseline delay parameterisation, URLLC and Holographic chains exceed their SLA budgets from zero load onward, ISAC+AIaaS exceeds its SLA beyond $\approx 14 \%$ network load, and only SecaaS+CaaS remains below its deterministic SLA budget up to approximately $\mathbf{6 5 \%}$ network load. Cross-layer Virtual Network Function (VNF) transitions increase latency by up to $40 \%$ per hop. Based on this per-hop penalty, we estimate as a design hypothesis that an AI-native orchestrator minimizing inter-layer placements could reduce SLA violation rates by $40 \%$ to $60 \%$ at moderate loads.
Engin Zeydan, Pasika Sashmal Ranaweera, Madhusanka Liyanage· 2026 IEEE International Conf...· 0 citations
Network function virtualization (NFV) decouples network functions from dedicated hardware by implementing them as software-based virtual network functions (VNFs), thereby enabling flexible network resource management. Network services are provisioned through service function chains (SFCs) composed of multiple VNFs. Given SFC requests and physical machines (PMs), this paper investigates the VNF deployment problem with three objectives: maximizing the service acceptance ratio (SAR), minimizing the total energy consumption (TEC), and balancing the load among PMs. We then propose a load-aware and energyefficient VNF deployment (LEEV) scheme that dynamically adapts its deployment strategy based on the system load. Under lightload conditions, LEEV deploys adjacent VNFs of a given SFC on one PM to reduce energy consumption, while under heavy-load conditions, it can shift toward load-balancing deployment without sacrificing energy efficiency. Simulation results reveal that LEEV achieves a higher SAR, lower TEC, and improved load balance.
You-Chiun Wang, Pei-Chin Tsai· International Conference on...· 0 citations