TriSLA：面向5G网络中结合可解释人工智能的多域决策的预防性闭环SLA感知架构
TriSLA: A Preventive and Closed-Loop SLA-Aware Architecture for Multidomain Decision-Making with Explainable Artificial Intelligence in 5G Networks
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中文总结 AI 辅助
TriSLA是面向5G多域决策的预防性闭环SLA感知架构，结合XAI实现100%SLA满意度，预测准确率达99.51%，处理开销小，可消除部署后SLA违规。
中文摘要 AI 辅助
多域5G环境中的网络切片在动态资源变异性和异构服务需求下，带来了保障服务水平协议（SLA）的关键挑战。本文提出TriSLA，这是一种闭环、预防性、SLA感知的架构，旨在在请求时评估可行性并在运行期间持续确保SLA合规性。该架构将本体驱动的语义意图解释、多域机器学习可行性风险推理、可解释人工智能（XAI）特征归因以及闭环运行时SLA保障整合为统一的操作流程。在集成了无线接入网（RAN）、传输网（TN）和5G核心网（5GC）的多节点云原生环境中，结合实时遥测数据采集，对完全可运行的原型进行了评估。实验评估表明，TriSLA对已接纳的切片实现了100%的SLA满意度，与反应式（51.2%）和静态阈值（80.4%）接纳基线相比，完全消除了部署后违规情况。预测可行性评估的分类准确率高达99.51%（默认可解释随机森林分类器为98.68%），支持在基础设施投入前做出预防性接纳决策。此外，认知接纳流程引入的处理开销极小：本体驱动语义解析需25.37毫秒，XAI辅助可行性推理需231.66毫秒。同时，闭环保障引擎在4.22秒的恢复周期内解决了100%的运行时遥测异常。这些结果表明，TriSLA通过集成预测性接纳和闭环运行时保障，为下一代5G网络提供了可靠、可解释、透明且预防性的SLA管理。
英文摘要
Network slicing in multidomain 5G environments introduces critical challenges in guaranteeing Service Level Agreements (SLAs) under dynamic resource variability and heterogeneous service requirements. This article presents TriSLA, a closed-loop, preventive, SLA-aware architecture designed to evaluate feasibility at request time and continuously ensure SLA compliance during operation. The architecture combines ontology-driven semantic intent interpretation, multidomain machine learning feasibility risk inference, Explainable Artificial Intelligence (XAI) feature attribution, and closed-loop runtime SLA assurance into a unified operational pipeline. A fully operational prototype was evaluated in a multi-node cloud-native environment integrating Radio Access Network (RAN), Transport Network (TN), and 5G Core (5GC) domains with real-time telemetry collection. Experimental evaluation demonstrates that TriSLA guarantees a 100% SLA satisfaction rate for admitted slices, completely eliminating post-deployment violations compared to reactive (51.2%) and static threshold (80.4%) admission baselines. The predictive feasibility assessment achieved a classification accuracy of up to 99.51% (98.68% for the default explainable Random Forest classifier), enabling preventive admission decisions before infrastructure commitment. Furthermore, the cognitive admission pipeline introduces minimal processing overhead, requiring 25.37 ms for ontology-driven semantic parsing and 231.66 ms for XAI-assisted feasibility inference. Concurrently, the closed-loop assurance engine resolves 100% of runtime telemetry anomalies within a 4.22 s recovery cycle. These results demonstrate that TriSLA provides reliable, explainable, transparent, and preventive SLA management through integrated predictive admission and closed-loop runtime assurance for next-generation 5G networks.
发表机构
- Universidade do Vale do Rio dos Sinos (UNISINOS)（圣路易斯河谷大学）
- Instituto Nacional de Telecomunicações (Inatel)（国家电信研究所）
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