AI 中文总结
研究边缘计算系统功能稳定性，提出从系统到单个功能的形式化框架，定义强弱稳定性，引入参数和指标系统，经定理联系，通过案例研究实例化，指标能区分传统可用性无法区分的架构选择。
AI 中文摘要
边缘计算系统运行于组件故障、间歇性回程连接、资源耗尽和对抗性干扰等正常运行环境中，而非罕见事件。传统可靠性和容错模型针对整体系统及二元工作/故障状态，无法满足资源受限边缘节点上共置服务的差异化质量需求。本文提出功能稳定性的形式化框架，将分析单元从系统转向单个功能。通过连续质量函数和每个功能的质量阈值定义强稳定性和弱稳定性。引入基本参数（干扰容忍度、恢复时间、降级深度、可接受降级）和聚合指标（累积质量、功能可用性、积分稳定性）系统，并通过九个定理将其与形式化定义相联系。该框架针对边缘计算场景实例化，并通过两个分析案例研究进行说明：无人机群和具有云回退的边缘AI推理服务。所提出的指标区分了传统可用性无法区分的架构选择，并揭示了架构排名对明确选择的干扰类集的依赖性。
英文摘要
Edge computing systems operate in conditions where component failures, intermittent backhaul connectivity, resource exhaustion and adversarial disturbances are part of the normal operating envelope rather than rare events. Classical reliability and fault-tolerance models, oriented toward monolithic systems and binary working/failed states, do not capture the differentiated quality requirements of services co-located on a resource-constrained edge node. This article proposes a formal framework of functional stability that shifts the unit of analysis from the system to the individual function. Strong and weak forms of stability are defined through a continuous quality function and per-function quality thresholds. A system of base parameters (disturbance tolerance, recovery time, degradation depth, admissible degradation) and aggregate metrics (cumulative quality, functional availability, integral stability) is introduced and connected to the formal definitions by nine theorems. The framework is instantiated for edge computing scenarios and illustrated through two analytical case studies: a UAV swarm and an edge AI inference service with cloud fallback. The proposed metrics distinguish architectural alternatives that classical availability cannot separate and expose the dependence of architectural ranking on the explicitly selected disturbance-class set.
Comments30 pages, 2 tabeles, 1 fig
