Delayed-Bottleneck Prediction: Predicting Future Performance Bottlenecks to Enable Proactive Computational Decision-Making

Computational systems execute sequences of operations whose effects on performance may extend beyond the immediate execution step. An operation that appears inexpensive or beneficial when evaluated locally may increase queue lengths, concentrate demand on shared resources, create dependencies, consume critical capacity, or produce an unfavorable system state that becomes a performance bottleneck several steps later. Conventional scheduling and resource-management approaches commonly rely on current system measurements, predefined policies, reactive threshold detection, or predictions of aggregate workload. Although these approaches can mitigate existing or anticipated congestion, predicting the delayed consequences of individual candidate operations remains an important problem in proactive computational decision-making. This paper proposes Delayed-Bottleneck Prediction (DBP), a conceptual framework for identifying candidate operations that may create future performance bottlenecks before their adverse effects become observable. DBP represents the computational system as a sequential state-transition process and evaluates candidate actions according to their predicted effects on future queue lengths, resource utilization, workload distribution, dependency structure, execution latency, and recovery requirements. A delayed-bottleneck evaluator estimates the probability, severity, and expected onset time of future bottlenecks under alternative actions. An uncertainty-aware decision mechanism then selects operations that balance immediate utility, predicted downstream performance, computational cost, and operational risk. The framework supports learned prediction, simulation-based lookahead, probabilistic forecasting, and receding-horizon decision-making. A mathematical formulation, learning objectives, inference algorithm, and experimental protocol are presented. No experimental results are claimed. The proposed framework constitutes a testable research hypothesis whose effectiveness and novelty must be established through systematic comparison with reactive scheduling, workload forecasting, predictive autoscaling, queue-aware control, and model-based planning methods.