Failure-Aware Predictive Computing for Generative AI: Anticipating Node Failures and Resource Shortages for Proactive Computational Task Migration

The increasing deployment of generative artificial intelligence (AI) systems has created computational infrastructures that depend on the continuous availability of heterogeneous GPU nodes, distributed inference services, memory resources, and network connections. Large language model (LLM) inference frequently involves long-running computational operations, substantial GPU memory consumption, autoregressive generation, and intermediate computational states that may be expensive to reconstruct. Consequently, unexpected node failures, GPU failures, memory exhaustion, resource contention, and infrastructure degradation can interrupt ongoing inference tasks, increase computational expenditure, and compromise service reliability. Although existing fault-tolerance mechanisms provide checkpointing, task restart, workload replication, and reactive migration, their operation is frequently triggered by failures or resource shortages that have already occurred. This paper introduces Failure-Aware Predictive Computing for Generative AI (FAPC-GAI), a conceptual framework that investigates whether impending infrastructure failures and resource shortages can be predicted sufficiently early to enable computational tasks to be migrated before service interruption occurs. The proposed framework integrates infrastructure telemetry acquisition, predictive failure modeling, resource shortage forecasting, risk-aware task assessment, migration decision optimization, destination-node selection, computational-state transfer, and post-migration verification. Unlike conventional reactive fault tolerance, FAPC-GAI attempts to transform infrastructure risk predictions into proactive computational continuity decisions. Its architecture distinguishes between hardware failure, software failure, GPU memory exhaustion, resource contention, network degradation, and anticipated capacity insufficiency, recognizing that different risk categories require different migration strategies and preparation times. A unified optimization formulation is developed to evaluate the expected benefits of migration against transfer overhead, destination resource consumption, migration latency, checkpointing expenditure, and the potential computational losses associated with delayed or unnecessary migration. Particular attention is given to the migration of LLM inference states, including model execution configurations, active request states, KV caches, generation progress, and other reusable computational information. The framework further investigates coordinated migration decisions within multi-node generative AI infrastructures, where destination selection must consider predicted failure risks, available GPU capacity, network bandwidth, workload compatibility, and service-level requirements. An experimental methodology is proposed to evaluate failure prediction accuracy, migration lead time, task interruption duration, computational recovery expenditure, successful task completion, service availability, and total infrastructure cost. The proposed framework does not assume that predictive migration is universally beneficial; its effectiveness depends on the predictability of infrastructure degradation, the available migration window, computational-state transfer costs, and the reliability of destination resources. By extending predictive computing toward anticipatory fault avoidance, this research explores a computational infrastructure in which generative AI systems do not merely recover after failures, but attempt to identify emerging infrastructure risks and relocate vulnerable workloads before those risks materialize.