Predictive Task Deadline Management for Generative AI: Forecasting Latest Safe Completion Times for Proactive Computational Scheduling

The increasing deployment of generative artificial intelligence (AI) systems has introduced computational environments in which inference workloads, resource availability, economic costs, and user demand fluctuate over time. Predictive computing provides a potential mechanism for anticipating future computational requirements and preparing reusable responses, model states, and other computational resources before actual requests arrive. However, identifying a future computational task and determining whether it should be executed in advance are insufficient for effective predictive orchestration. An additional question concerns when each anticipated task must be completed to preserve its expected computational value and satisfy subsequent service requirements. This paper introduces Predictive Task Deadline Management for Generative AI (PTDM-GAI), a conceptual framework that investigates the prediction of latest safe completion times for anticipated computational workloads. Unlike conventional deadline scheduling, which generally operates on explicitly specified task deadlines or established service-level constraints, PTDM-GAI estimates task-specific completion deadlines from predicted request arrival distributions, expected computational execution times, resource availability, information freshness, preparation dependencies, and service requirements. The framework distinguishes between predicted demand arrival time, preferred preparation time, latest safe completion time, and externally imposed execution deadlines. Its architecture integrates future demand forecasting, task completion-time estimation, deadline inference, uncertainty-aware scheduling, deadline-sensitive resource allocation, and continuous deadline recalibration. A mathematical formulation is developed to estimate the latest completion time under probabilistic demand and execution uncertainty while accounting for the economic costs of early preparation, deadline violations, resource contention, and unused speculative computation. The framework further investigates the interaction between task deadlines and economic-aware predictive computing, particularly when computational workloads can be transferred to off-peak periods or lower-cost cloud GPU resources. An experimental methodology is proposed to evaluate deadline prediction accuracy, deadline compliance, computational savings, preparation lead time, resource utilization, and overall economic efficiency. The proposed research does not assume that predicted deadlines can always be estimated accurately or that earlier completion is universally beneficial. Rather, it investigates whether dynamically predicting the latest safe completion time of future computational workloads can provide a more systematic basis for coordinating proactive inference preparation and resource scheduling. Its practical effectiveness remains subject to empirical validation.