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. 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%.

本文提出了一种基于联邦传输组件分析的方法，通过训练生成对抗网络模型并使用已有和目标虚拟网络功能的数据，实现了资源消耗的预测，同时保护数据隐私。实验结果表明，该方法能有效地减少回归模型的均方根误差指标，并提高决定系数度量值。

面向有效的 VNF 分析的联邦传输组件分析

Federated Transfer Component Analysis Towards Effective VNF Profiling

Digital Twin (DT) is a prominent technology to utilise and deploy within the
healthcare sector. Yet, the main challenges facing such applications are:
Strict health data-sharing policies, high-performance network requirements, and
possible infrastructure resource limitations. In this paper, we address all the
challenges by provisioning adaptive Virtual Network Functions (VNFs) to enforce
security policies associated with different data-sharing scenarios. We define a
Cloud-Native Network orchestrator on top of a multi-node cluster mesh
infrastructure for flexible and dynamic container scheduling. The proposed
framework considers the intended data-sharing use case, the policies
associated, and infrastructure configurations, then provision Service Function
Chaining (SFC) and provides routing configurations accordingly with little to
no human intervention. Moreover, what is \textit{optimal} when deploying SFC is
dependent on the use case itself, and we tune the hyperparameters to prioritise
resource utilisation or latency in an effort to comply with the performance
requirements. As a result, we provide an adaptive network orchestration for
digital health twin use cases, that is policy-aware, requirements-aware, and
resource-aware.

本文提出一种针对数字健康孪生应用的自适应网络编排框架，通过提供适应性的虚拟网络功能以实施安全策略、创建服务功能链和提供路由配置，以此来应对医疗保健部门严格的健康信息共享政策、高性能网络需求和可能存在的基础设施资源限制等主要挑战。

数字健康双生体用例的自适应服务函数链编排：启发式增强 Q 学习方法

Adaptive Services Function Chain Orchestration For Digital Health Twin  Use Cases: Heuristic-boosted Q-Learning Approach

Service Function Chaining (SFC) allows the forwarding of a traffic flow along
a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT).
Software Defined Networking (SDN) solutions can be used to support SFC reducing
the management complexity and the operational costs. One of the most critical
issues for the service and network providers is the reduction of energy
consumption, which should be achieved without impact to the quality of
services. In this paper, we propose a novel resource (re)allocation
architecture which enables energy-aware SFC for SDN-based networks. To this
end, we model the problems of VNF placement, allocation of VNFs to flows, and
flow routing as optimization problems. Thereafter, heuristic algorithms are
proposed for the different optimization problems, in order find near-optimal
solutions in acceptable times. The performance of the proposed algorithms are
numerically evaluated over a real-world topology and various network traffic
patterns. The results confirm that the proposed heuristic algorithms provide
near optimal solutions while their execution time is applicable for real-life
networks.

本文提出了一种新颖的资源（重新）分配体系结构，通过模型化 VNF 部署、流和流路由的优化问题，提出了启发式算法实现能量感知 SFC，评估结果证实了所提出的启发式算法在可接受的时间内提供了接近于最优的解决方案，适用于实际网络。