Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)
Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite utility into a single closed-loop policy.The framework continuously redistributes computational budget across a population of heterogeneous AI models so that the global utility, defined jointly ove...
| Authors | Rahul Umesh Mhapsekar, Ilias Cherkaoui, Lizy Abraham et al. |
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| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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