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.

The emergence of beyond 5G and 6G networks is set to revolutionise telecommunications, addressing the demands of emerging applications through advanced capabilities. At the core of this transformation lies next-generation intelligent service orchestration, which is essential for meeting future Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) such as ultra-low latency, efficient power consumption and resource utilisation. These capabilities require multi-objective, seamless end-to-end service delivery across complex, distributed environments. Achieving such delivery requires scalable and modular system design approaches that support dynamic service composition and adaptability. Cloud-native technologies, underpinned by microservices architectures, plays a pivotal role, but also will introduce challenges in orchestrating resources efficiently across heterogeneous domains. To address these challenges, this paper proposes a solution, Federated Intelligent multi-objective Service function chain Orchestration (FISO) that integrates multi-objective federated profiling to preserve privacy while ensuring efficient end-to-end service delivery. FISO integrates Federated Learning (FL) and Reinforcement Learning (RL). FL is used to collaboratively learn from distributed edge profiling clients without sharing raw data, while RL dynamically guides optimal decision making for resource allocation and Service Function Chain (SFC) placement based on feedback from the federated models. FISO predicts optimal computing and network resources for SFCs, enabling the selection of appropriate edge locations, efficient resource allocation, placement of SFCs, and lifecycle management. Experimental results demonstrated on a pragmatic testbed validate the effectiveness of FISO in efficiently placing requested SFCs within an administrative domain with multiple edge/cloud nodes, predicting optimal CPU, memory, and link capacity resources, and minimising end-to-end latency and energy consumption.

The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a Federated Transfer Component Analysis (FTCA) method between the source and target VNFs. FTCA first trains Generative Adversarial Networks (GANs) based on the source VNF profiling data, and the trained GANs model is sent to the target VNF domain. Then, FTCA realizes federated domain adaptation by using the generated source VNF data and less target VNF profiling data, while keeping the raw data locally. The proposed FTCA enables efficient profiling knowledge transfer among different VNFs, while maintaining data privacy. Through FTCA, faster new VNF deployment can be expected. Experiments show that the proposed FTCA can effectively predict the required resources for the target VNF. Specifically, the RMSE index of the regression model decreases by 38.5% and the R-squared metric advances up to 68.6%.

Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given its flat networking approach. This paper introduces the privacy-preserving K8s operator kubeFlower. It addresses privacy risks via isolation-by-design and differential privacy for data management. Isolation ensures secure resource sharing, while differential privacy safeguards individual data privacy. We introduce the Privacy Preserving Persistent Volume Claimer (P3-VC), which adds noise to data while managing a privacy budget. kubeFlower simplifies FL system management in K8s while ensuring privacy. We tested our approach on a network testbed composed of different geo-located cloud and edge nodes where FL clients are deployed. Our results demonstrate the approach’s efficacy in preserving privacy in K8s-based FL for cloud–edge environments.

Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different user data sets have significant similarities on the feature spaces, the final goal is to build a high-performance global model. However, not all features are great contributors when training the global HFL model, some features even impair the HFL. Besides, the curse of dimension will delay the training time and cause more energy consumption (EC). In this case, it is critical to remove irrelevant features from the local and select the useful overlapping features from a federated global perspective. In addition, the uncertainty of data being labeled and the nonindependent and identically distributed (non-IID) client data should also consider. This article introduces an unsupervised federated feature selection approach (named FSHFL) for HFL in IoT networks. First, a feature relevance outlier detection method is applied to the HFL participants to remove the useless features, which combines with the improved one-class support vector machine. Besides, a feature relevance hierarchical clustering (FRHC) algorithm is proposed for HFL overlapping feature selection. Experiment results on four IoT data sets show that the proposed methods can select better-federated feature sets among HFL participants, thus improving the performance of the HFL system. Specifically, the global model accuracy improves up to 1.68% since fewer irrelevant features. Moreover, FSHFL can lower the average training time as high as 6.9%. Finally, when the global model gets the same test accuracy, FSHFL can decrease the average EC of training the model by approximately 2.85% compared to federated average and roughly 68.39% compared to Fed-SGD.

With the development of Industrial Internet of Things (IIoT), the computation intensive tasks with restrict delay constraints generated at network edge emerge as the main challenge to the terminals with limited power and processing capability. The combination of edge and cloud computing has been demonstrated as one promising solution to such problem. In the edge-cloud collaboration (ECC) framework, the task scheduling among edges and cloud is of great importance on impacting the performance of the system. In this paper, we investigate the task offloading strategy to minimize the energy consumption of the networks. To achieve that, a time delay penalty mechanism, which searches the optimal power for edge to cloud task offloading under given delay constraint, is first proposed. On that basis, a low complexity edge-cloud matching algorithm leveraging the bipartite matching method is developed, to further minimize the execution energy consumption of all devices. Finally, to evaluate its efficiency, the proposed algorithm is deployed and tested on an novel edge-cloud computing collaboration platform. Both simulation and experiment results revel that our proposed scheme can achieve the less energy consumption compared with other alternatives. In addition, it also indicates that our proposed scheme can effectively matching resources from the edge to the cloud, especially for the issues that edge devices fail to meet demands due to limit processing ability.

With the rapid development of the Internet of Things (IoT) technology, how to meet the execution requirements of sensitive services has become a key point to be solved in application scenarios such as smart cities and Internet of Vehicles. Combining with the advantages over edge computing and cloud computing, building the edge-cloud collaboration framework is currently a hot research area. In this paper, a Raspberry Pi edge-cloud collaboration framework (RPECCF) is proposed to effectively reply the complicated application requirements in multi-scenes. Furthermore, to evaluate the performance of the RPECCF, we develop an experiment platform. Experimental results show the RPECCF platform is stable, also allocate the edge and cloud resources properly. The proof-of-concept demonstration of the platform is studied in terms of task latency and framerate of both edge only, edge-cloud collaboration, and cloud only.