Resilient Fog-Cloud Architectures and Fault-Tolerant Test Infrastructure for Large-Scale GPU Manufacturing: Integrating Edge Intelligence, Energy Prediction, and Verification Paradigms

Abstract

This article synthesizes contemporary conceptual foundations and applied methodologies for designing resilient fog–cloud computing architectures and fault-tolerant test infrastructure targeted at large-scale GPU manufacturing. The abstract summarizes the research problem, methodological approach, principal findings, and implications. Background: modern GPU manufacturing and deployment increasingly rely on distributed computation across cloud, fog, and edge tiers for tasks including in-line inspection, predictive power management, real-time quality control, and cryptographically secure telemetry aggregation (Prakash, Suresh, & Dhinesh Kumar, 2019; Deepika & Prakash, 2020). Problem statement: existing test infrastructures for GPUs often fail to simultaneously satisfy the competing demands of scalability, low latency, energy efficiency, and rigorous formal verification (Designing Fault-Tolerant Test Infrastructure for Large-Scale GPU Manufacturing, 2025; Huang, Tsai, Paul, & Chen, 2005). Methods: this work develops an integrative framework combining fog computing paradigms, machine-learning based power prediction, hybrid cloud storage and serverless HPC patterns, and formal verification tools (Labelled Transition System Analyser; Lambers, Ehrig & Orejas, 2006; Lohmann, Sauer & Engels, 2005). Results: descriptive analysis demonstrates how layered redundancy, consensus-inspired decision rules, and blockchain-augmented telemetry can jointly improve fault tolerance, reduce energy volatility, and maintain throughput under realistic manufacturing perturbations (Iyer, 2020; Yehia & Aljaafreh, 2023). Discussion: the paper interprets findings in light of trade-offs between latency, trust, and computational cost, examines limitations of current verification approaches, and maps a research agenda for automated model checking and fog-native testing. Conclusion: by synthesizing theoretical and applied work across distributed computing and verification communities, the proposed architecture offers a practical route to robust, cost-effective, and verifiable GPU testbeds capable of meeting future manufacturing scale-up.