在线SLA分解：实现对演进中网络系统的实时适应
Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems
AI总结：
针对网络切片跨多技术域时E2E SLA分解的动态适应问题，提出基于在线梯度下降和FIFO内存缓冲区的在线学习-分解框架，持续更新风险模型，在变化条件下实现比静态方法更准确、更具弹性的SLA分解。
摘要：
当网络切片跨越多个技术域时，每个域维护与该切片相关的端到端（E2E）服务等级协议（SLA）至关重要。因此，E2E SLA必须被正确分解为部分SLA，并分配给涉及的每个域。在包含E2E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访问本地控制器对先前请求响应的历史数据，并利用这些信息为每个域构建风险模型。在本研究中，我们扩展了之前的工作，研究真实世界系统的动态特性，并引入在线学习-分解框架来应对这种动态性。我们提出了一个基于最新反馈持续更新风险模型的框架。该方法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和鲁棒性。我们在基于分析模型的模拟器上的实证研究表明，所提出的框架优于最先进的静态方法，在变化的条件和数据限制下提供了更准确、更具弹性的SLA分解。此外，我们还对所提解决方案进行了全面的复杂性分析。
英文摘要：
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 only 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 continuously 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, delivering more accurate and resilient SLA decomposition under varying conditions and data limitations. Furthermore, we provide a comprehensive complexity analysis of the proposed solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
