Generative AI: Anticipating Execution Interruptions and Preparing Recoverable Computational States

The increasing deployment of generative artificial intelligence (AI) systems has introduced computational workloads characterized by long-running inference requests, continuously growing key-value (KV) caches, heterogeneous GPU availability, distributed execution dependencies, and fluctuating infrastructure reliability. Although existing fault-tolerance mechanisms employ periodic checkpointing, state replication, and restart-based recovery to reduce the consequences of computational interruptions, these mechanisms frequently operate according to fixed checkpoint intervals or reactive failure detection. Consequently, computational states may be lost when interruptions occur between checkpoints, while excessively frequent checkpointing introduces additional computation, memory transfer, storage, and communication overhead. This paper introduces Predictive Computational Recovery for Generative AI (PCR-GAI), a framework that investigates whether computational interruption risks can be anticipated and used to determine when, what, and where computational states should be preserved before an interruption occurs. Rather than treating checkpointing as an exclusively periodic or failure-triggered operation, PCR-GAI integrates predictive interruption modeling, computational-state assessment, adaptive checkpoint scheduling, recoverability preparation, and risk-aware recovery orchestration. Its architecture considers multiple interruption sources, including cloud GPU preemption, hardware degradation, GPU memory pressure, node failures, communication disruptions, runtime instability, scheduled maintenance, and resource reallocation. The framework estimates the probability and potential timing of interruptions, evaluates the computational value of currently available execution states, and selectively preserves recoverable information according to expected computational loss and checkpoint expenditure. A hierarchical state-preservation mechanism distinguishes lightweight execution metadata, token-generation progress, reusable KV cache blocks, intermediate computational representations, and distributed execution state. A unified optimization formulation is developed to balance expected recomputation loss, checkpoint generation cost, storage expenditure, state-transfer latency, recovery time, and service-level requirements. Furthermore, the framework investigates how predictive recovery can interact with broader predictive computing mechanisms, including advance computational preparation, GPU workload forecasting, economic-aware resource allocation, task migration, and predictive scheduling. An experimental methodology is proposed to evaluate interruption prediction quality, checkpoint efficiency, recovery completeness, lost computation, recovery latency, total GPU expenditure, and end-to-end service reliability. The proposed research does not assume that predictive checkpointing is universally beneficial. Its effectiveness depends on the predictability of interruption events, the availability of sufficient warning time, the cost of preserving computational states, and the technical feasibility of restoring those states under compatible execution conditions. By extending predictive computing from anticipating future computational demand to anticipating the potential loss of ongoing computation, this research establishes a conceptual foundation for interruption-aware and recovery-oriented generative AI infrastructure.