Mathematical Modeling of Hardware System Availability and Failure Prognosis in Large-Scale AI/ML Clusters (International Journal of Engineering and Computer Science, 2025)
The reliability and availability of hardware components in large-scale AI/ML clusters are crucial for maintaining computational efficiency, minimizing downtime, and ensuring uninterrupted operations. Given the highly distributed nature of AI/ML environments, which consist of thousands of interconnected processors, GPUs, TPUs, storage units, and networking devices, robust mathematical models are necessary to predict failures, optimize resource allocation, and improve system resilience. This study explores a range of mathematical approaches for modeling hardware reliability and system availability, including failure rate analysis using Weibull distributions, system state transitions using Markov models, and predictive failure prognosis through Bayesian networks. The integration of historical failure data and real-time telemetry enables proactive failure detection, allowing for preemptive maintenance strategies. Additionally, various availability metrics are examined to enhance system uptime and performance. Queueing theory models, particularly M/M/1 models, help determine system availability based on mean time to failure (MTTF) and mean time to repair (MTTR), ensuring optimal workload distribution and redundancy management. Fault tree analysis (FTA) is employed to assess failure dependencies and evaluate the impact of redundant system configurations, such as RAID storage and GPU replication, on overall cluster availability. Furthermore, stochastic Petri nets (SPNs) are utilized to dynamically model concurrent failure and repair processes, providing insights into system resilience under varying operational conditions. By integrating these mathematical frameworks, AI/ML clusters can improve their fault tolerance, enhance predictive maintenance strategies, and achieve higher operational efficiency. This research offers a comprehensive methodology for optimizing hardware reliability in AI/ML infrastructures, reducing the risk of unexpected failures and ensuring sustained computational throughput. The findings presented contribute to the ongoing development of intelligent, self-healing AI/ML systems capable of adapting to evolving hardware challenges.