An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
arXiv:2605.10718v2 Announce Type: replace-cross Abstract: Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge-tier environments. The framework employs parallel micro-agents that integrate the free-energy principle, causal do-
The proliferation of complex, distributed computing environments in the computing continuum necessitates more robust and autonomous failure diagnosis and mitigation, a gap current approaches struggle to fill.
Reliable operation of AI and deeply integrated computing systems across the edge requires systems capable of self-diagnosis and resilience, preventing widespread outages and ensuring service continuity.
This research introduces micro-agents for proactive, uncertainty-aware diagnosis of 'grey failures', shifting from reactive debugging to predictive and autonomous system resilience.
- · Edge AI providers
- · Cloud infrastructure companies
- · Industrial IoT operators
- · AI developers
- · Traditional IT support models
- · Systems heavily reliant on human-driven diagnostics
- · Cloud providers with poor grey failure handling
Increased reliability and uptime of edge computing and AI inference at the network periphery.
Reduced operational costs for distributed computing infrastructure due to automated resilience, accelerating edge AI adoption.
Enhanced trust in autonomous systems, enabling more critical applications to be deployed on distributed, self-managing infrastructure.
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Read at arXiv cs.LG