论文标题
在延迟关键NFV应用程序用例中，基于图形神经网络的基于图形的主动SLA管理框架
A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case
论文作者
论文摘要
5G和6G推出的最新进展导致了通过网络功能虚拟化（NFV）实现灵活和软焊接通信网络的范式提供的新范围关键延迟应用程序的出现。不断发展的垂直行业，例如电信，智能电网，虚拟现实（VR），工业4.0，自动化车辆等。由低潜伏期和高可靠性的愿景驱动，并且有很大的空白可以有效地弥合服务提供者和最终用户的服务质量（QOS）约束。在这项工作中，我们希望通过提出一个主动的SLA管理框架来利用图形神经网络（GNN）和深度强化学习（DRL）来平衡效率和可靠性之间的权衡处置，以解决对潜伏至关重要服务的过度提供。为了总结我们的关键贡献：1）我们在多输血方案中组成了基于图的时空多元时间序列预测模型，并具有多个时步预测，在使用案例上确定的基线最先进的基线最新基线模型中，可以提高74.62％的性能； 2）我们利用现实的SLA定义为用例使用DRL来扩展策略管理，以实现动态的SLA感知监督。
Recent advancements in the rollout of 5G and 6G have led to the emergence of a new range of latency-critical applications delivered via a Network Function Virtualization (NFV) enabled paradigm of flexible and softwarized communication networks. Evolving verticals like telecommunications, smart grid, virtual reality (VR), industry 4.0, automated vehicles, etc. are driven by the vision of low latency and high reliability, and there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for both the service providers and the end-user. In this work, we look to tackle the over-provisioning of latency-critical services by proposing a proactive SLA management framework leveraging Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to balance the trade-off between efficiency and reliability. To summarize our key contributions: 1) we compose a graph-based spatio-temporal multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, delivering 74.62% improved performance over the established baseline state-of-art model on the use-case; and 2) we leverage realistic SLA definitions for the use-case to achieve a dynamic SLA-aware oversight for scaling policy management with DRL.
