用主动推理机制实现分布式智能系统的自愈能力
Resilient by Design -- Active Inference for Distributed Continuum Intelligence
- 基于因果故障图与自由能原理，动态识别设备故障
- 通过主动推理实现自主修复，保障系统持续稳定运行
- 适合高复杂度分布式系统故障应对场景
在从资源受限的物联网和边缘节点到高性能计算系统的分布式计算连续体（DCC）中，故障是常态。确保跨层级的可靠性和全局一致性对依赖实时自适应协调的AI工作负载仍是重大挑战。本文提出概率主动推理韧性代理（PAIR-Agent），实现DCC系统的韧性。该代理执行三项核心操作：(i) 从设备日志构建因果故障图；(ii) 利用马尔可夫毯和自由能原理，在管理确定性与不确定性中识别故障；(iii) 通过主动推理自主修复问题。通过持续监控与自适应重构，该代理在多种故障条件下保持服务连续性与稳定性。理论验证确认了所提框架的可靠性与有效性。
原文摘要 · Abstract (English)
Failures are the norm in highly complex and heterogeneous devices spanning the distributed computing continuum (DCC), from resource-constrained IoT and edge nodes to high-performance computing systems. Ensuring reliability and global consistency across these layers remains a major challenge, especially for AI-driven workloads requiring real-time, adaptive coordination. This work-in-progress paper introduces a Probabilistic Active Inference Resilience Agent (PAIR-Agent) to achieve resilience in DCC systems. PAIR-Agent performs three core operations: (i) constructing a causal fault graph from device logs, (ii) identifying faults while managing certainties and uncertainties using Markov blankets and the free energy principle, and (iii) autonomously healing issues through active inference. Through continuous monitoring and adaptive reconfiguration, the agent maintains service continuity and stability under diverse failure conditions. Theoretical validations confirm the reliability and effectiveness of the proposed framework.
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