When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access solely to historical data regarding the responses of local controllers to previous requests, and this information is used to construct a risk model for each domain. In this study, we extend our previous work by investigating the dynamic nature of real-world systems and introducing an online learning-decomposition framework to tackle the dynamicity. We propose a framework that periodically updates the risk models based on the most recent feedback. This approach leverages key components such as online gradient descent and FIFO memory buffers, which enhance the stability and robustness of the overall process. Our empirical study on an analytic model-based simulator demonstrates that the proposed framework outperforms the state-of-the-art static approach, providing more accurate and resilient SLA decomposition even under varying conditions and limited data scenarios.

翻译：当网络切片跨越多个技术域时，每个域都必须维护与该切片相关的端到端服务等级协议。因此，端到端SLA必须被适当地分解为部分SLA，并分配给每个相关域。在一个包含端到端服务编排器和本地域控制器的两级架构网络切片管理系统中，我们假设编排器仅能获取关于本地控制器对先前请求响应的历史数据，并利用这些数据为每个域构建风险模型。本研究在先前工作基础上，进一步探究现实系统的动态特性，并引入一种在线学习-分解框架以应对动态变化。我们提出一种周期性基于最新反馈更新风险模型的框架，该框架利用在线梯度下降和FIFO内存缓冲区等关键组件，增强了整体过程的稳定性与鲁棒性。基于分析模型的仿真实验表明，所提框架优于当前最先进的静态方法，即使在多变条件和有限数据场景下，仍能提供更精确、更具弹性的SLA分解。