AI-Driven Digital Twin Modeling for Predictive Resource Scheduling and Fault-Tolerant Optimization in Cloud-Native Systems
Keywords:
digital twin; cloud-native systems; predictive scheduling; fault tolerance; resource optimization; governance; sustainabilityAbstract
Cloud-native systems have transformed service delivery by enabling elastic, containerized workloads, but their operational complexity often exceeds the capacity of conventional resource management approaches. This paper examines AI-driven digital twin modeling as a structural mechanism for predictive resource scheduling and fault-tolerant optimization. Rather than treating digital twins as isolated simulation artifacts, the paper positions them as continuous virtual representations that mirror runtime infrastructure, application dependency graphs, and control-plane decisions. The analysis focuses on system-level trade-offs involving observability, state synchronization, model fidelity, inference latency, governance, and resilience. A central argument is that effective digital twin architectures must combine lightweight real-time telemetry with probabilistic learning methods that capture workload uncertainty. The paper further discusses how predictive schedulers can use digital twin projections to reduce resource contention and preempt failure cascades. Fault tolerance is addressed through redundant control loops, degraded state awareness, and policy-based reconfiguration. Cross-domain comparisons with industrial and scientific computing illustrate how cloud-native environments differ from traditional high-performance computing. The discussion extends to sustainability, fairness, and regulatory implications. The paper concludes that digital twin modeling offers a promising but demanding path toward closed-loop operational intelligence, requiring careful integration of data governance, model lifecycle management, and platform engineering.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
